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Full-Text Articles in Databases and Information Systems

Destress: Computation-Optimal And Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization, Boyue Li, Zhize Li, Yuejie Chi Aug 2022

Destress: Computation-Optimal And Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization, Boyue Li, Zhize Li, Yuejie Chi

Research Collection School Of Computing and Information Systems

Emerging applications in multiagent environments such as internet-of-things, networked sensing, autonomous systems, and federated learning, call for decentralized algorithms for finite-sum optimizations that are resource efficient in terms of both computation and communication. In this paper, we consider the prototypical setting where the agents work collaboratively to minimize the sum of local loss functions by only communicating with their neighbors over a predetermined network topology. We develop a new algorithm, called DEcentralized STochastic REcurSive gradient methodS (DESTRESS) for nonconvex finite-sum optimization, which matches the optimal incremental first-order oracle complexity of centralized algorithms for finding first-order stationary points, while maintaining communication …

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Self-Adaptive Systems: A Systematic Literature Review Across Categories And Domains, Terence Wong, Markus Wagner, Christoph Treude Aug 2022

Self-Adaptive Systems: A Systematic Literature Review Across Categories And Domains, Terence Wong, Markus Wagner, Christoph Treude

Research Collection School Of Computing and Information Systems

Context: Championed by IBM’s vision of autonomic computing paper in 2003, the autonomic computing research field has seen increased research activity over the last 20 years. Several conferences (SEAMS, SASO, ICAC) and workshops (SISSY) have been established and have contributed to the autonomic computing knowledge base in search of a new kind of system — a self-adaptive system (SAS). These systems are characterized by being context-aware and can act on that awareness. The actions carried out could be on the system or on the context (or environment). The underlying goal of a SAS is the sustained achievement of its goals …

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Crosscbr: Cross‑View Contrastive Learning For Bundle Recommendation, Yunshan Ma, Yingzhi He, An Zhang, Xiang Wang, Tat-Seng Chua Aug 2022

Crosscbr: Cross‑View Contrastive Learning For Bundle Recommendation, Yunshan Ma, Yingzhi He, An Zhang, Xiang Wang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Bundle recommendation aims to recommend a bundle of related items to users, which can satisfy the users' various needs with one-stop convenience. Recent methods usually take advantage of both user-bundle and user-item interactions information to obtain informative representations for users and bundles, corresponding to bundle view and item view, respectively. However, they either use a unified view without differentiation or loosely combine the predictions of two separate views, while the crucial cooperative association between the two views' representations is overlooked.In this work, we propose to model the cooperative association between the two different views through cross-view contrastive learning. By encouraging …

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Developing Gamification Research In Information Systems, De Liu, Richard Landers, Paul Lowry, Fiona Fui-Hoon Nah, Radhika Santhanam Aug 2022

Developing Gamification Research In Information Systems, De Liu, Richard Landers, Paul Lowry, Fiona Fui-Hoon Nah, Radhika Santhanam

Research Collection School Of Computing and Information Systems

Gamification, an umbrella term for incorporating game design elements into non-game systems to make them more engaging and productive, is of interest to Information System (IS) communities, because it has wide applications, such as, gamification designs for workplace, learning and health apps, social media, online communities, and the gig economy. As a multidisciplinary research domain, gamification integrates elements of technology, human motivation, task design, human-computer-interface design, and algorithms/AI design, and is a fertile ground for IS researchers with a variety of different background. There are many challenges in conducting gamification research, including how to get started in this highly multidisciplinary …

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Extract Human Mobility Patterns Powered By City Semantic Diagram, Zhangqing Shan, Weiwei Shan, Baihua Zheng Aug 2022

Extract Human Mobility Patterns Powered By City Semantic Diagram, Zhangqing Shan, Weiwei Shan, Baihua Zheng

Research Collection School Of Computing and Information Systems

With widespread deployment of GPS devices, massive spatiotemporal trajectories became more accessible. This booming trend paved the solid data ground for researchers to discover the regularities or patterns of human mobility. However, there are still three challenges in semantic pattern extraction including semantic absence, semantic bias and semantic complexity. In this paper, we invent and apply a novel data structure namely City Semantic Diagram to overcome above three challenges. First, our approach resolves semantic absence by exactly identifying semantic behaviours from raw trajectories. Second, the delicate design of semantic purification helps us to detect semantic complexity from human mobility. Third, …

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Ransomsoc: A More Effective Security Operations Center To Detect And Respond To Ransomware Attacks, Anthony Cheuk Tung Lai, Ping Fan Ke, Kelvin Chan, Siu Ming Yiu, Dongsun Kim, Wai Kin Wong, Shuai Wang, Joseph Muppala, Alan Ho Aug 2022

Ransomsoc: A More Effective Security Operations Center To Detect And Respond To Ransomware Attacks, Anthony Cheuk Tung Lai, Ping Fan Ke, Kelvin Chan, Siu Ming Yiu, Dongsun Kim, Wai Kin Wong, Shuai Wang, Joseph Muppala, Alan Ho

Research Collection School Of Computing and Information Systems

Ransomware remains a major threat for organizations. Despite a lot of research done, existing solutions still have at least two shortcomings. (I) Slow detection time: by the time we realize that the system is under ransomware attack, almost all files have been encrypted. (II) Without a ransomwareaware backup scheme: Most existing systems, in particular those in SMEs (small and medium enterprises), do not have a proper backup system. Even they have it, either it is not a remote-site backup (i.e., files in the backup system may also be encrypted) or it is not designed for ransomware attacks. In this paper, …

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Interpreting Trajectories From Multiple Views: A Hierarchical Self-Attention Network For Estimating The Time Of Arrival, Zebin Chen, Xiaolin Xiao, Yue-Jiao Gong, Jun Fang, Nan Ma, Hua Chai, Zhiguang Cao Aug 2022

Interpreting Trajectories From Multiple Views: A Hierarchical Self-Attention Network For Estimating The Time Of Arrival, Zebin Chen, Xiaolin Xiao, Yue-Jiao Gong, Jun Fang, Nan Ma, Hua Chai, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Estimating the time of arrival is a crucial task in intelligent transportation systems. Although considerable efforts have been made to solve this problem, most of them decompose a trajectory into several segments and then compute the travel time by integrating the attributes from all segments. The segment view, though being able to depict the local traffic conditions straightforwardly, is insufficient to embody the intrinsic structure of trajectories on the road network. To overcome the limitation, this study proposes multi-view trajectory representation that comprehensively interprets a trajectory from the segment-, link-, and intersection-views. To fulfill the purpose, we design a hierarchical …

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Variational Graph Author Topic Modeling, Ce Zhang, Hady Wirawan Lauw Aug 2022

Variational Graph Author Topic Modeling, Ce Zhang, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

While Variational Graph Auto-Encoder (VGAE) has presented promising ability to learn representations for documents, most existing VGAE methods do not model a latent topic structure and therefore lack semantic interpretability. Exploring hidden topics within documents and discovering key words associated with each topic allow us to develop a semantic interpretation of the corpus. Moreover, documents are usually associated with authors. For example, news reports have journalists specializing in writing certain type of events, academic papers have authors with expertise in certain research topics, etc. Modeling authorship information could benefit topic modeling, since documents by the same authors tend to reveal …

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Multimodal Private Signatures, Khoa Nguyen, Fuchun Guo, Willy Susilo, Guomin Yang Aug 2022

Multimodal Private Signatures, Khoa Nguyen, Fuchun Guo, Willy Susilo, Guomin Yang

Research Collection School Of Computing and Information Systems

We introduce Multimodal Private Signature (MPS) - an anonymous signature system that offers a novel accountability feature: it allows a designated opening authority to learn some partial information op about the signer’s identity id, and nothing beyond. Such partial information can flexibly be defined as op = id (as in group signatures), or as op = 0 (like in ring signatures), or more generally, as op = Gj (id), where Gj (·) is a certain disclosing function. Importantly, the value of op is known in advance by the signer, and hence, the latter can decide whether she/he wants to disclose …

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Investigating Toxicity Changes Of Cross-Community Redditors From 2 Billion Posts And Comments, Hind Almerekhi, Haewoon Kwak, Bernard J. Jansen Aug 2022

Investigating Toxicity Changes Of Cross-Community Redditors From 2 Billion Posts And Comments, Hind Almerekhi, Haewoon Kwak, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

This research investigates changes in online behavior of users who publish in multiple communities on Reddit by measuring their toxicity at two levels. With the aid of crowdsourcing, we built a labeled dataset of 10,083 Reddit comments, then used the dataset to train and fine-tune a Bidirectional Encoder Representations from Transformers (BERT) neural network model. The model predicted the toxicity levels of 87,376,912 posts from 577,835 users and 2,205,581,786 comments from 890,913 users on Reddit over 16 years, from 2005 to 2020. This study utilized the toxicity levels of user content to identify toxicity changes by the user within the …

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Trajectory Optimization For Safe Navigation In Maritime Traffic Using Historical Data, Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar, T. K. Satish Kumar Aug 2022

Trajectory Optimization For Safe Navigation In Maritime Traffic Using Historical Data, Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar, T. K. Satish Kumar

Research Collection School Of Computing and Information Systems

Increasing maritime trade often results in congestion in busy ports, thereby necessitating planning methods to avoid close quarter risky situations among vessels. Rapid digitization and automation of port operations and vessel navigation provide unique opportunities for significantly improving navigation safety. Our key contributions are as follows. First, given a set of future candidate trajectories for vessels in a traffic hotspot zone, we develop a multiagent trajectory optimization method to choose trajectories that result in the best overall close quarter risk reduction. Our novel MILP-based optimization method is more than an order-of-magnitude faster than a standard MILP for this problem, and …

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Simple And Optimal Stochastic Gradient Methods For Nonsmooth Nonconvex Optimization, Zhize Li, Jian Li Aug 2022

Simple And Optimal Stochastic Gradient Methods For Nonsmooth Nonconvex Optimization, Zhize Li, Jian Li

Research Collection School Of Computing and Information Systems

We propose and analyze several stochastic gradient algorithms for finding stationary points or local minimum in nonconvex, possibly with nonsmooth regularizer, finite-sum and online optimization problems. First, we propose a simple proximal stochastic gradient algorithm based on variance reduction called ProxSVRG+. We provide a clean and tight analysis of ProxSVRG+, which shows that it outperforms the deterministic proximal gradient descent (ProxGD) for a wide range of minibatch sizes, hence solves an open problem proposed in Reddi et al. (2016b). Also, ProxSVRG+ uses much less proximal oracle calls than ProxSVRG (Reddi et al., 2016b) and extends to the online setting by …

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Efficient Resource Allocation With Fairness Constraints In Restless Multi-Armed Bandits, Dexun Li, Pradeep Varakantham Aug 2022

Efficient Resource Allocation With Fairness Constraints In Restless Multi-Armed Bandits, Dexun Li, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Restless Multi-Armed Bandits (RMAB) is an apt model to represent decision-making problems in public health interventions (e.g., tuberculosis, maternal, and child care), anti-poaching planning, sensor monitoring, personalized recommendations and many more. Existing research in RMAB has contributed mechanisms and theoretical results to a wide variety of settings, where the focus is on maximizing expected value. In this paper, we are interested in ensuring that RMAB decision making is also fair to different arms while maximizing expected value. In the context of public health settings, this would ensure that different people and/or communities are fairly represented while making public health intervention …

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Systems And Methods For Contrastive Learning With Self-Labeling Refinement, Pan Zhou, Caiming Xiong, Steven Hoi Aug 2022

Systems And Methods For Contrastive Learning With Self-Labeling Refinement, Pan Zhou, Caiming Xiong, Steven Hoi

Research Collection School Of Computing and Information Systems

Embodiments described herein provide a contrastive learning mechanism with self - labeling refinement , which iteratively employs the network and data themselves to generate more accurate and informative soft labels for contrastive learning . Specifically , the contrastive learning framework includes a self - labeling refinery module to explicitly generate accurate labels , and a momentum mix - up module to increase similarity between a query and its positive , which in turn implicitly improves label accuracy.

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Evaluation Of Automation Techniques For Data Quality Assessment For Party And Product Master Data, Mahmood Mohammed Jul 2022

Evaluation Of Automation Techniques For Data Quality Assessment For Party And Product Master Data, Mahmood Mohammed

Theses and Dissertations

In an era, where data is being used by organizations in achieving their business goals and driving their business decisions, it is key to ensure the quality of data. For an organization, the most important data assets include master data assets such as product master data and customer master data, supplier master data, employee master data which can be generalized as party master data. There has been significant growth and variety in data in recent years because of which the traditional rules-based approach and dependency on data experts for assessing data quality is no longer working. This dissertation evaluates and …

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A Low-Power Passive Uhf Tag With High-Precision Temperature Sensor For Human Body Application, Liang-Hung Wang, Zheng Pan, Hao Jiang, Hua-Ling Lai, Qi-Peng Ran, Patricia Angela R. Abu Jul 2022

A Low-Power Passive Uhf Tag With High-Precision Temperature Sensor For Human Body Application, Liang-Hung Wang, Zheng Pan, Hao Jiang, Hua-Ling Lai, Qi-Peng Ran, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Radio frequency identification (RFID) tags are widely used in various electronic devices due to their low cost, simple structure, and convenient data reading. This topic aims to study the key technologies of ultra-high frequency (UHF) RFID tags and high-precision temperature sensors, and how to reduce the power consumption of the temperature sensor and the overall circuits while maintaining minimal loss of performance. Combined with the biomedicine, an innovative high-precision human UHF RFID chip for body temperature monitoring is designed. In this study, a ring oscillator whose output frequency is linearly related to temperature is designed and proposed as a temperature-sensing …

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Machine Learning Approach For Classifying Power Outage In Secondary Electric Distribution Network, Stephan Mgaya, Hellen Maziku Jul 2022

Machine Learning Approach For Classifying Power Outage In Secondary Electric Distribution Network, Stephan Mgaya, Hellen Maziku

Tanzania Journal of Engineering and Technology (TJET)

Power outage is the problem that hinders social and economic development especially for developing countries like Tanzania. Frequent power outages damage electric equipment, and negatively affect the industrial production process. Power outages cannot be completely eradicated due to uncontrolled cause like natural calamities but technical challenges can be managed and hence reducing power outages. The existing manual methods used to locate power outage like customer calls is inefficient and time consuming. On the other hand, modern method like the Advanced Metering Infrastructure (AMI) still faces a challenge in effectively classifying power line outage due to the nature of imbalanced datasets. …

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Big Data Analytics Framework For Effective Higher Education Institutions, George Matto Jul 2022

Big Data Analytics Framework For Effective Higher Education Institutions, George Matto

Tanzania Journal of Engineering and Technology (TJET)

There has been an increased dependency on Information and Communication Technologies (ICTs) in undertaking various activities in Higher Education Institutions (HEIs) ecosystems. Because of that, huge volumes of data have increasingly been generated. There have been, for instance, considerable amounts of data generated through electronic platforms involved in students’ admission and registration process, students’ academic records management, teaching and learning data, curriculum related data, and several other administrative data. Analysis of data generated from these platforms stands to give students, lecturers, HEIs Management, policy makers and implementers, and other stakeholders useful insights that would help in improving HEIs’ effectiveness. Unfortunately, …

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An Iot Based Worker Safety Helmet Using Cloud Computing Technology, Yuthriya Iddi Khamis, Mahmoud Alawi, Waheed Muhammad Sanya Jul 2022

An Iot Based Worker Safety Helmet Using Cloud Computing Technology, Yuthriya Iddi Khamis, Mahmoud Alawi, Waheed Muhammad Sanya

Tanzania Journal of Engineering and Technology (TJET)

Miner safety and security is a major challenge around the world due to the exposure to toxic gases that are frequently released in underground mines. Miners' health is adversely affected primarily by toxic gases, which endanger the workers' lives. Furthermore, human sensory abilities do not detect these dangerous gases. As a result, this paper proposes a safety monitoring system that includes a temperature sensor, humidity sensor, and gas sensors to detect harmful gases and alert miners to those harmful gases using the smart helmet they wear. These gases are transmitted to the control station via the cloud using Internet of …

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Reflection As An Agile Course Evaluation Tool, Siaw Ling Lo, Pei Hua Cher, Fernando Bello Jul 2022

Reflection As An Agile Course Evaluation Tool, Siaw Ling Lo, Pei Hua Cher, Fernando Bello

Research Collection School Of Computing and Information Systems

Reflection is often used as a tool to analyse student learning, be it for internalizing of acquired knowledge or as a form of seeking help through expression of doubts or misconceptions. However, it can be a challenge to extract relevant information from the free-form reflection text. Often times the workload of manually analyzing the reflection text can be a form of deterrence instead of providing insights in the course delivery for instructors, let alone improving the learning experience. In this paper, we review the current usage of reflection and propose an automated reflection framework, together with an end-to-end analysis of …

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Using Constraint Programming And Graph Representation Learning For Generating Interpretable Cloud Security Policies, Mikhail Kazdagli, Mohit Tiwari, Akshat Kumar Jul 2022

Using Constraint Programming And Graph Representation Learning For Generating Interpretable Cloud Security Policies, Mikhail Kazdagli, Mohit Tiwari, Akshat Kumar

Research Collection School Of Computing and Information Systems

Modern software systems rely on mining insights from business sensitive data stored in public clouds. A data breach usually incurs signifcant (monetary) loss for a commercial organization. Conceptually, cloud security heavily relies on Identity Access Management (IAM) policies that IT admins need to properly confgure and periodically update. Security negligence and human errors often lead to misconfguring IAM policies which may open a backdoor for attackers. To address these challenges, frst, we develop a novel framework that encodes generating optimal IAM policies using constraint programming (CP). We identify reducing dormant permissions of cloud users as an optimality criterion, which intuitively …

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Strategic Signaling For Utility Control In Audit Games, Jianan Chen, Qin Hu, Honglu Jiang Jul 2022

Strategic Signaling For Utility Control In Audit Games, Jianan Chen, Qin Hu, Honglu Jiang

Informatics and Engineering Systems Faculty Publications

As an effective method to protect the daily access to sensitive data against malicious attacks, the audit mechanism has been widely deployed in various practical fields. In order to examine security vulnerabilities and prevent the leakage of sensitive data in a timely manner, the database logging system usually employs an online signaling scheme to issue an alert when suspicious access is detected. Defenders can audit alerts to reduce potential damage. This interaction process between a defender and an attacker can be modeled as an audit game. In previous studies, it was found that sending real-time signals in the audit …

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Self-Guided Learning To Denoise For Robust Recommendation, Yunjun Gao, Yuntao Du, Yujia Hu, Lu Chen, Xinjun Zhu, Ziquan Fang, Baihua Zheng Jul 2022

Self-Guided Learning To Denoise For Robust Recommendation, Yunjun Gao, Yuntao Du, Yujia Hu, Lu Chen, Xinjun Zhu, Ziquan Fang, Baihua Zheng

Research Collection School Of Computing and Information Systems

The ubiquity of implicit feedback makes them the default choice to build modern recommender systems. Generally speaking, observed interactions are considered as positive samples, while unobserved interactions are considered as negative ones. However, implicit feedback is inherently noisy because of the ubiquitous presence of noisy-positive and noisy-negative interactions. Recently, some studies have noticed the importance of denoising implicit feedback for recommendations, and enhanced the robustness of recommendation models to some extent. Nonetheless, they typically fail to (1) capture the hard yet clean interactions for learning comprehensive user preference, and (2) provide a universal denoising solution that can be applied to …

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Lead1.0: A Large-Scale Annotated Dataset For Energy Anomaly Detection In Commercial Buildings, Manoj Gulati, Pandarasamy Arjunan Jul 2022

Lead1.0: A Large-Scale Annotated Dataset For Energy Anomaly Detection In Commercial Buildings, Manoj Gulati, Pandarasamy Arjunan

Research Collection School Of Computing and Information Systems

Modern buildings are densely equipped with smart energy meters, which periodically generate a massive amount of time-series data yielding a few million data points every day. This data can be leveraged to discover the underlying load and infer their energy consumption patterns, inter-dependencies on environmental factors, and the building’s operational properties. Furthermore, it allows us to simultaneously identify anomalies present in the electricity consumption profiles, which is a big step towards saving energy and achieving global sustainability. However, to date, the lack of large-scale annotated energy consumption datasets hinders the ongoing research in anomaly detection. We contribute to this effort …

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Towards Aligning Slides And Video Snippets: Mitigating Sequence And Content Mismatches, Ziyuan Liu, Hady W. Lauw Jul 2022

Towards Aligning Slides And Video Snippets: Mitigating Sequence And Content Mismatches, Ziyuan Liu, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Slides are important form of teaching materials used in various courses at academic institutions. Due to their compactness, slides on their own may not stand as complete reference materials. To aid students’ understanding, it would be useful to supplement slides with other materials such as online videos. Given a deck of slides and a related video, we seek to align each slide in the deck to a relevant video snippet, if any. While this problem could be formulated as aligning two time series (each involving a sequence of text contents), we anticipate challenges in generating matches arising from differences in …

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Declaration-Based Prompt Tuning For Visual Question Answering, Yuhang Liu, Wei Wei, Feida Zhu, Feida Zhu Jul 2022

Declaration-Based Prompt Tuning For Visual Question Answering, Yuhang Liu, Wei Wei, Feida Zhu, Feida Zhu

Research Collection School Of Computing and Information Systems

In recent years, the pre-training-then-fine-tuning paradigm has yielded immense success on a wide spectrum of cross-modal tasks, such as visual question answering (VQA), in which a visual-language (VL) model is first optimized via self-supervised task objectives, e.g., masked language modeling (MLM) and image-text matching (ITM), and then fine-tuned to adapt to downstream task (e.g., VQA) via a brand-new objective function, e.g., answer prediction. However, the inconsistency of the objective forms not only severely limits the generalization of pre-trained VL models to downstream tasks, but also requires a large amount of labeled data for fine-tuning. To alleviate the problem, we propose …

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Multi-Level Cross-View Contrastive Learning For Knowledge-Aware Recommender System, Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu, Xin Cao Jul 2022

Multi-Level Cross-View Contrastive Learning For Knowledge-Aware Recommender System, Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu, Xin Cao

Research Collection School Of Computing and Information Systems

Knowledge graph (KG) plays an increasingly important role in recommender systems. Recently, graph neural networks (GNNs) based model has gradually become the theme of knowledge-aware recommendation (KGR). However, there is a natural deficiency for GNN-based KGR models, that is, the sparse supervised signal problem, which may make their actual performance drop to some extent. Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring the contrastive learning in KG-aware recommendation and propose a novel multi-level cross-view contrastive learning mechanism, named MCCLK. Different from traditional contrastive learning methods which …

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Finding Top-M Leading Records In Temporal Data, Yiyi Wang Jul 2022

Finding Top-M Leading Records In Temporal Data, Yiyi Wang

Dissertations and Theses Collection (Open Access)

A traditional top-k query retrieves the records that stand out at a certain point in time. On the other hand, a durable top-k query considers how long the records retain their supremacy, i.e., it reports those records that are consistently among the top-k in a given time interval. In this thesis, we introduce a new query to the family of durable top-k formulations. It finds the top-m leading records, i.e., those that rank among the top-k for the longest duration within the query interval. Practically, this query assesses the records based on how long …

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Contours Of Virtual Enfreakment In Fighting Game Characters, Sercan Sengun, Peter Mawhorter, James Bowie-Wilson, Yusef Audeh, Haewoon Kwak, D. Fox Harrell Jul 2022

Contours Of Virtual Enfreakment In Fighting Game Characters, Sercan Sengun, Peter Mawhorter, James Bowie-Wilson, Yusef Audeh, Haewoon Kwak, D. Fox Harrell

Research Collection School Of Computing and Information Systems

Characters in fighting videogames1 such as Street Fighter V and Tekken7 typically reveal a phenomenon that we define as virtual enfreakment: their bodies, costumes, and fighting styles are exaggerated (1) in a manner that emphasizes perceived exoticism and (2) to enable them to be easily visually and conceptually distinguishable from one another. Here, using both quantitative and qualitative methods, including crowd-sourced surveys and analyses of game mechanics, we report on the contours of virtual enfreakment in those games. We specifically examine differences in character design across gender, national-origin, and skin-color lines. Disappointingly but not surprisingly, we find racism and sexism …

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Multi-Agent Reinforcement Learning For Traffic Signal Control Through Universal Communication Method, Qize Jiang, Minhao Qin, Shengmin Shi, Weiwei Sun Sun, Baihua Zheng Jul 2022

Multi-Agent Reinforcement Learning For Traffic Signal Control Through Universal Communication Method, Qize Jiang, Minhao Qin, Shengmin Shi, Weiwei Sun Sun, Baihua Zheng

Research Collection School Of Computing and Information Systems

How to coordinate the communication among intersections effectively in real complex traffic scenarios with multi-intersection is challenging. Existing approaches only enable the communication in a heuristic manner without considering the content/importance of information to be shared. In this paper, we propose a universal communication form UniComm between intersections. UniComm embeds massive observations collected at one agent into crucial predictions of their impact on its neighbors, which improves the communication efficiency and is universal across existing methods. We also propose a concise network UniLight to make full use of communications enabled by UniComm. Experimental results on real datasets demonstrate that UniComm …

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