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Vietnamese Punctuation Prediction Using Deep Neural Networks, Thuy Pham, Nhu Nguyen, Hong Quang Pham, Han Cao, Binh Nguyen
Vietnamese Punctuation Prediction Using Deep Neural Networks, Thuy Pham, Nhu Nguyen, Hong Quang Pham, Han Cao, Binh Nguyen
Research Collection School Of Computing and Information Systems
Adding appropriate punctuation marks into text is an essential step in speech-to-text where such information is usually not available. While this has been extensively studied for English, there is no large-scale dataset and comprehensive study in the punctuation prediction problem for the Vietnamese language. In this paper, we collect two massive datasets and conduct a benchmark with both traditional methods and deep neural networks. We aim to publish both our data and all implementation codes to facilitate further research, not only in Vietnamese punctuation prediction but also in other related fields. Our project, including datasets and implementation details, is publicly …
Coupled Rain Streak And Background Estimation Via Separable Element-Wise Attention, Yinjie Tan, Qiang Wen, Jing Qin, Jianbo Jiao, Guoqiang Han, Shengfeng He
Coupled Rain Streak And Background Estimation Via Separable Element-Wise Attention, Yinjie Tan, Qiang Wen, Jing Qin, Jianbo Jiao, Guoqiang Han, Shengfeng He
Research Collection School Of Computing and Information Systems
Single image de-raining is challenging especially in the scenarios with dense rain streaks. Existing methods resolve this problem by predicting the rain streaks of the image, which constrains the network to focus on local rain streaks features. However, dense rain streaks are visually similar to mist or fog (with large intensities), in this case, the training objective should be shifted to image recovery instead of extracting rain streaks. In this paper, we propose a coupled rain streak and background estimation network that explores the intrinsic relations between two tasks. In particular, our network produces task-dependent feature maps, each part of …
Neighbourhood Structure Preserving Cross-Modal Embedding For Video Hyperlinking, Yanbin Hao, Chong-Wah Ngo, Benoit Huet
Neighbourhood Structure Preserving Cross-Modal Embedding For Video Hyperlinking, Yanbin Hao, Chong-Wah Ngo, Benoit Huet
Research Collection School Of Computing and Information Systems
Video hyperlinking is a task aiming to enhance the accessibility of large archives, by establishing links between fragments of videos. The links model the aboutness between fragments for efficient traversal of video content. This paper addresses the problem of link construction from the perspective of cross-modal embedding. To this end, a generalized multi-modal auto-encoder is proposed.& x00A0;The encoder learns two embeddings from visual and speech modalities, respectively, whereas each of the embeddings performs self-modal and cross-modal translation of modalities. Furthermore, to preserve the neighbourhood structure of fragments, which is important for video hyperlinking, the auto-encoder is devised to model data …
A Survey Of Spatial Crowdsourcing, Yongxin Tong, Zimu Zhou, Yuxiang Zeng, Lei Chen, Cyrus Shahabi
A Survey Of Spatial Crowdsourcing, Yongxin Tong, Zimu Zhou, Yuxiang Zeng, Lei Chen, Cyrus Shahabi
Research Collection School Of Computing and Information Systems
Crowdsourcing is a computing paradigm where humans are actively involved in a computing task, especially for tasks that are intrinsically easier for humans than for computers. Spatial crowdsourcing (SC) is an increasing popular category of crowdsourcing in the era of mobile Internet and sharing economy, where tasks are spatiotemporal and must be completed at a specific location and time. In fact, spatial crowdsourcing has stimulated a series of recent industrial successes including sharing economy for urban services (Uber and Gigwalk) and spatiotemporal data collection (OpenStreetMap and Waze). This survey dives deep into the challenges and techniques brought by the unique …
An Exact Single-Agent Task Selection Algorithm For The Crowdsourced Logistics, Chung-Kyun Han, Shih-Fen Cheng
An Exact Single-Agent Task Selection Algorithm For The Crowdsourced Logistics, Chung-Kyun Han, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
The trend of moving online in the retail industry has created great pressure for the logistics industry to catch up both in terms of volume and response time. On one hand, volume is fluctuating at greater magnitude, making peaks higher; on the other hand, customers are also expecting shorter response time. As a result, logistics service providers are pressured to expand and keep up with the demands. Expanding fleet capacity, however, is not sustainable as capacity built for the peak seasons would be mostly vacant during ordinary days. One promising solution is to engage crowdsourced workers, who are not employed …
Systematic Classification Of Attackers Via Bounded Model Checking, Eric Rothstein-Morris, Jun Sun, Sudipta Chattopadyay
Systematic Classification Of Attackers Via Bounded Model Checking, Eric Rothstein-Morris, Jun Sun, Sudipta Chattopadyay
Research Collection School Of Computing and Information Systems
In this work, we study the problem of verification of systems in the presence of attackers using bounded model checking. Given a system and a set of security requirements, we present a methodology to generate and classify attackers, mapping them to the set of requirements that they can break. A naive approach suffers from the same shortcomings of any large model checking problem, i.e., memory shortage and exponential time. To cope with these shortcomings, we describe two sound heuristics based on cone-of-influence reduction and on learning, which we demonstrate empirically by applying our methodology to a set of hardware benchmark …
Practical Server-Side Indoor Localization: Tackling Cardinality Outlier Challenges, Anuradha Ravi, Archan Misra
Practical Server-Side Indoor Localization: Tackling Cardinality Outlier Challenges, Anuradha Ravi, Archan Misra
Research Collection School Of Computing and Information Systems
In spite of many advances in indoor localization techniques, practical implementation of robust device independent, server-side Wi-Fi localization (i.e., without any active participation of client devices) remains a challenge. This work utilizes an operationally-deployed Wi-Fi based indoor location infrastructure, based on the classical RADAR algorithm, to tackle two such practical challenges: (a) low cardinality, whereby only the associated AP generates sufficient RSSI reports and (b) outlier identification, which requires explicit identification of mobile clients that are attached to the Wi-Fi network but outside the fingerprinted region. To tackle the low-cardinality problem, we present a technique that uses cardinality changes to …
Scalable, Adaptable And Fast Estimation Of Transient Downtime In Virtual Infrastructures Using Convex Decomposition And Sample Path Randomization, Zhiling Guo, Jin Li, Ram Ramesh
Scalable, Adaptable And Fast Estimation Of Transient Downtime In Virtual Infrastructures Using Convex Decomposition And Sample Path Randomization, Zhiling Guo, Jin Li, Ram Ramesh
Research Collection School Of Computing and Information Systems
Network function virtualization enables efficient cloud-resource planning by virtualizing network services and applications into software running on commodity servers. A cloud-service provider needs to manage and ensure service availability of a network of concurrent virtualized network functions (VNFs). The downtime distribution of a network of VNFs can be estimated using sample-path randomization on the underlying birth–death process. An integrated modeling approach for this purpose is limited by its scalability and computational load because of the high dimensionality of the integrated birth–death process. We propose a generalized convex decomposition of the integrated birth-death process, which transforms the high-dimensional multi-VNF process into …
Server-Aided Revocable Attribute-Based Encryption For Cloud Computing Services, Hui Cui, Tsz Hon Yuen, Robert H. Deng, Guilin Wang
Server-Aided Revocable Attribute-Based Encryption For Cloud Computing Services, Hui Cui, Tsz Hon Yuen, Robert H. Deng, Guilin Wang
Research Collection School Of Computing and Information Systems
Attribute-based encryption (ABE) has been regarded as a promising solution in cloud computing services to enable scalable access control without compromising the security. Despite of the advantages, efficient user revocation has been a challenge in ABE. One suggestion for user revocation is using the binary tree in the key generation phase of an ABE scheme, which enables a trusted key generation center to periodically distribute the key update information to all nonrevoked users over a public channel. This revocation approach reduces the size of key updates from linear to logarithmic in the number of users. But it requires each user …
Spatial Multi-Objective Land Use Optimization Toward Livability Based On Boundary-Based Genetic Algorithm: A Case Study In Singapore, Kai Cao, Muyang Liu, Shu Wang, Mengqi Liu, Wenting Zhang, Qiang Meng, Bo Huang
Spatial Multi-Objective Land Use Optimization Toward Livability Based On Boundary-Based Genetic Algorithm: A Case Study In Singapore, Kai Cao, Muyang Liu, Shu Wang, Mengqi Liu, Wenting Zhang, Qiang Meng, Bo Huang
Research Collection School Of Computing and Information Systems
In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by a boundary-based genetic algorithm adapted to help address the spatial multi-objective land use optimization problem. Furthermore, the model is successfully and effectively applied to the case study in the Central Region of Queenstown Planning Area of Singapore towards livability. In the case study, the experiments based on equal weights and experiments based on different weights combination have …
Structure-Priority Image Restoration Through Genetic Algorithm Optimization, Zhaoxia Wang, Haibo Pen, Ting Yang, Quan Wang
Structure-Priority Image Restoration Through Genetic Algorithm Optimization, Zhaoxia Wang, Haibo Pen, Ting Yang, Quan Wang
Research Collection School Of Computing and Information Systems
With the significant increase in the use of image information, image restoration has been gaining much attention by researchers. Restoring the structural information as well as the textural information of a damaged image to produce visually plausible restorations is a challenging task. Genetic algorithm (GA) and its variants have been applied in many fields due to their global optimization capabilities. However, the applications of GA to the image restoration domain still remain an emerging discipline. It is still challenging and difficult to restore a damaged image by leveraging GA optimization. To address this problem, this paper proposes a novel GA-based …
Lightweight Sharable And Traceable Secure Mobile Health System, Yang Yang, Ximeng Liu, Robert H. Deng, Yingjiu Li
Lightweight Sharable And Traceable Secure Mobile Health System, Yang Yang, Ximeng Liu, Robert H. Deng, Yingjiu Li
Research Collection School Of Computing and Information Systems
Mobile health (mHealth) has emerged as a new patient centric model which allows real-time collection of patient data via wearable sensors, aggregation and encryption of these data at mobile devices, and then uploading the encrypted data to the cloud for storage and access by healthcare staff and researchers. However, efficient and scalable sharing of encrypted data has been a very challenging problem. In this paper, we propose a Lightweight Sharable and Traceable (LiST) secure mobile health system in which patient data are encrypted end-to-end from a patient’s mobile device to data users. LiST enables efficient keyword search and finegrained access …
Remote Communication In Wilderness Search And Rescue: Implications For The Design Of Emergency Distributed-Collaboration Tools For Network-Sparse Environments, Brennan Jones, Anthony Tang, Carman Neustaedter
Remote Communication In Wilderness Search And Rescue: Implications For The Design Of Emergency Distributed-Collaboration Tools For Network-Sparse Environments, Brennan Jones, Anthony Tang, Carman Neustaedter
Research Collection School Of Computing and Information Systems
Wilderness search and rescue (WSAR) requires careful communication between workers in different locations. To understand the contexts from which WSAR workers communicate and the challenges they face, we interviewed WSAR workers and observed a mock-WSAR scenario. Our findings illustrate that WSAR workers face challenges in maintaining a shared mental model. This is primarily done through distributed communication using two-way radios and cell phones for text and photo messaging; yet both implicit and explicit communication suffer. WSAR workers send messages for various reasons and share different types of information with varying levels of urgency. This warrants the use of multiple communication …
Vireo @ Video Browser Showdown 2020, Phuong Anh Nguyen, Jiaxin Wu, Chong-Wah Ngo, Danny Francis, Benoit Huet
Vireo @ Video Browser Showdown 2020, Phuong Anh Nguyen, Jiaxin Wu, Chong-Wah Ngo, Danny Francis, Benoit Huet
Research Collection School Of Computing and Information Systems
In this paper, we present the features implemented in the 4th version of the VIREO Video Search System (VIREO-VSS). In this version, we propose a sketch-based retrieval model, which allows the user to specify a video scene with objects and their basic properties, including color, size, and location. We further utilize the temporal relation between video frames to strengthen this retrieval model. For text-based retrieval module, we supply speech and on-screen text for free-text search and upgrade the concept bank for concept search. The search interface is also re-designed targeting the novice user. With the introduced system, we expect that …
Automatically Categorizing Software Technologies, Mathieu Nassif, Christoph Treude, Martin P. Robillard
Automatically Categorizing Software Technologies, Mathieu Nassif, Christoph Treude, Martin P. Robillard
Research Collection School Of Computing and Information Systems
Informal language and the absence of a standard taxonomy for software technologies make it difficult to reliably analyze technology trends on discussion forums and other on-line venues. We propose an automated approach called Witt for the categorization of software technologies (an expanded version of the hypernym discovery problem). Witt takes as input a phrase describing a software technology or concept and returns a general category that describes it (e.g., integrated development environment), along with attributes that further qualify it (commercial, php, etc.). By extension, the approach enables the dynamic creation of lists of all technologies of a given type (e.g., …
Deterministic Identity-Based Encryption From Lattice-Based Programmable Hash Functions With High Min-Entropy, Daode Zhang, Jie Li, Bao Li, Xianhui Lu, Haiyang Xue, Dingding Jia, Yamin Liu
Deterministic Identity-Based Encryption From Lattice-Based Programmable Hash Functions With High Min-Entropy, Daode Zhang, Jie Li, Bao Li, Xianhui Lu, Haiyang Xue, Dingding Jia, Yamin Liu
Research Collection School Of Computing and Information Systems
There only exists one deterministic identity-based encryption (DIBE) scheme which is adaptively secure in the auxiliary-input setting, under the learning with errors (LWE) assumption. However, the master public key consists of basic matrices. In this paper, we consider to construct adaptively secure DIBE schemes with more compact public parameters from the LWE problem. (i) On the one hand, we gave a generic DIBE construction from lattice-based programmable hash functions with high min-entropy. (ii) On the other hand, when instantiating our generic DIBE construction with four LPHFs with high min-entropy, we can get four adaptively secure DIBE schemes with more compact …
The Topicality Of The Learning Organization: Is The Concept Still Relevant Today?, Siu Loon Hoe
The Topicality Of The Learning Organization: Is The Concept Still Relevant Today?, Siu Loon Hoe
Research Collection School Of Computing and Information Systems
This chapter introduces the current level of interest in the learning organization and usefulness of the concept at the present time. It reviews authors who have recently and explicitly commented on the topicality of the learning organization, offers a qualitative content analysis of recent journal publications on learning organizations justifying the need for the concept, and uses quantitative research using print media indicators and Google Trends to identify the number of publications related to the learning organization over time. The results suggest that while the level of interest in the learning organization among scientific researchers has grown, the level of …
Teacher-Student Networks With Multiple Decoders For Solving Math Word Problem, Jipeng Zhang, Roy Ka-Wei Lee, Ee-Peng Lim, Wei Qin, Lei Wang, Jie Shao, Qianru Sun
Teacher-Student Networks With Multiple Decoders For Solving Math Word Problem, Jipeng Zhang, Roy Ka-Wei Lee, Ee-Peng Lim, Wei Qin, Lei Wang, Jie Shao, Qianru Sun
Research Collection School Of Computing and Information Systems
Math word problem (MWP) is challenging due to the limitation in training data where only one “standard” solution is available. MWP models often simply fit this solution rather than truly understand or solve the problem. The generalization of models (to diverse word scenarios) is thus limited. To address this problem, this paper proposes a novel approach, TSN-MD, by leveraging the teacher network to integrate the knowledge of equivalent solution expressions and then to regularize the learning behavior of the student network. In addition, we introduce the multiple-decoder student network to generate multiple candidate solution expressions by which the final answer …
Deepdrawing: A Deep Learning Approach To Graph Drawing, Yong Wang, Zhihua Jin, Qianwen Wang, Weiwei Cui, Tengfei Ma, Huamin Qu
Deepdrawing: A Deep Learning Approach To Graph Drawing, Yong Wang, Zhihua Jin, Qianwen Wang, Weiwei Cui, Tengfei Ma, Huamin Qu
Research Collection School Of Computing and Information Systems
Node-link diagrams are widely used to facilitate network explorations. However, when using a graph drawing technique to visualize networks, users often need to tune different algorithm-specific parameters iteratively by comparing the corresponding drawing results in order to achieve a desired visual effect. This trial and error process is often tedious and time-consuming, especially for non-expert users. Inspired by the powerful data modelling and prediction capabilities of deep learning techniques, we explore the possibility of applying deep learning techniques to graph drawing. Specifically, we propose using a graph-LSTM-based approach to directly map network structures to graph drawings. Given a set of …
Planningvis: A Visual Analytics Approach To Production Planning In Smart Factories, Dong Sun, Renfei Huang, Yuanzhe Chen, Yong Wang, Jia Zeng, Mingxuan Yuan, Ting-Chuen Pong, Huamin Qu
Planningvis: A Visual Analytics Approach To Production Planning In Smart Factories, Dong Sun, Renfei Huang, Yuanzhe Chen, Yong Wang, Jia Zeng, Mingxuan Yuan, Ting-Chuen Pong, Huamin Qu
Research Collection School Of Computing and Information Systems
Production planning in the manufacturing industry is crucial for fully utilizing factory resources (e.g., machines, raw materials and workers) and reducing costs. With the advent of industry 4.0, plenty of data recording the status of factory resources have been collected and further involved in production planning, which brings an unprecedented opportunity to understand, evaluate and adjust complex production plans through a data-driven approach. However, developing a systematic analytics approach for production planning is challenging due to the large volume of production data, the complex dependency between products, and unexpected changes in the market and the plant. Previous studies only provide …
Towards Automated Infographic Design: Deep Learning-Based Auto-Extraction Of Extensible Timeline, Zhutian Chen, Yun Wang, Qianwen Wang, Yong Wang, Huamin Qu
Towards Automated Infographic Design: Deep Learning-Based Auto-Extraction Of Extensible Timeline, Zhutian Chen, Yun Wang, Qianwen Wang, Yong Wang, Huamin Qu
Research Collection School Of Computing and Information Systems
Designers need to consider not only perceptual effectiveness but also visual styles when creating an infographic. This process can be difficult and time consuming for professional designers, not to mention non-expert users, leading to the demand for automated infographics design. As a first step, we focus on timeline infographics, which have been widely used for centuries. We contribute an end-to-end approach that automatically extracts an extensible timeline template from a bitmap image. Our approach adopts a deconstruction and reconstruction paradigm. At the deconstruction stage, we propose a multi-task deep neural network that simultaneously parses two kinds of information from a …
A Review Of Emotion Sensing: Categorization Models And Algorithms, Zhaoxia Wang, Seng-Beng Ho, Erik Cambria
A Review Of Emotion Sensing: Categorization Models And Algorithms, Zhaoxia Wang, Seng-Beng Ho, Erik Cambria
Research Collection School Of Computing and Information Systems
Sentiment analysis consists in the identification of the sentiment polarity associated with a target object, such as a book, a movie or a phone. Sentiments reflect feelings and attitudes, while emotions provide a finer characterization of the sentiments involved. With the huge number of comments generated daily on the Internet, besides sentiment analysis, emotion identification has drawn keen interest from different researchers, businessmen and politicians for polling public opinions and attitudes. This paper reviews and discusses existing emotion categorization models for emotion analysis and proposes methods that enhance existing emotion research. We carried out emotion analysis by inviting experts from …
Entity-Sensitive Attention And Fusion Network For Entity-Level Multimodal Sentiment Classification, Jianfei Yu, Jing Jiang
Entity-Sensitive Attention And Fusion Network For Entity-Level Multimodal Sentiment Classification, Jianfei Yu, Jing Jiang
Research Collection School Of Computing and Information Systems
Entity-level (aka target-dependent) sentiment analysis of social media posts has recently attracted increasing attention, and its goal is to predict the sentiment orientations over individual target entities mentioned in users' posts. Most existing approaches to this task primarily rely on the textual content, but fail to consider the other important data sources (e.g., images, videos, and user profiles), which can potentially enhance these text-based approaches. Motivated by the observation, we study entity-level multimodal sentiment classification in this article, and aim to explore the usefulness of images for entity-level sentiment detection in social media posts. Specifically, we propose an Entity-Sensitive Attention …
Pgas: Privacy-Preserving Graph Encryption For Accurate Constrained Shortest Distance Queries, Can Zhang, Liehuang Zhu, Kashif Sharif, Chuan Zhang, Ximeng Liu
Pgas: Privacy-Preserving Graph Encryption For Accurate Constrained Shortest Distance Queries, Can Zhang, Liehuang Zhu, Kashif Sharif, Chuan Zhang, Ximeng Liu
Research Collection School Of Computing and Information Systems
The constrained shortest distance (CSD) query is used to determine the shortest distance between two vertices of a graph while ensuring that the total cost remains lower than a given threshold. The virtually unlimited storage and processing capabilities of cloud computing have enabled the graph owners to outsource their graph data to cloud servers. However, it may introduce privacy challenges that are difficult to address. In recent years, some relevant schemes that support the shortest distance query on the encrypted graph have been proposed. Unfortunately, some of them have unacceptable query accuracy, and some of them leak sensitive information that …
Synthesizing Aspect-Driven Recommendation Explanations From Reviews, Trung-Hoang Le, Hady W. Lauw
Synthesizing Aspect-Driven Recommendation Explanations From Reviews, Trung-Hoang Le, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Explanations help to make sense of recommendations, increasing the likelihood of adoption. However, existing approaches to explainable recommendations tend to rely on rigid, standardized templates, customized only via fill-in-the-blank aspect sentiments. For more flexible, literate, and varied explanations covering various aspects of interest, we synthesize an explanation by selecting snippets from reviews, while optimizing for representativeness and coherence. To fit target users' aspect preferences, we contextualize the opinions based on a compatible explainable recommendation model. Experiments on datasets of several product categories showcase the efficacies of our method as compared to baselines based on templates, review summarization, selection, and text …
Optimal Management Of Virtual Infrastructures Under Flexible Cloud Service Agreements, Zhiling Guo, Jin Li, Ram Ramesh
Optimal Management Of Virtual Infrastructures Under Flexible Cloud Service Agreements, Zhiling Guo, Jin Li, Ram Ramesh
Research Collection School Of Computing and Information Systems
A cloud service agreement entails the provisioning of a required set of virtual infrastructure resources at a specified level of availability to a client. The agreement also lays out the price charged to the client and a penalty to the provider when the assured availability is not met. The availability assurance involves backup resource provisioning, and the provider needs to allocate backups cost-effectively by balancing the resource-provisioning costs with the potential penalty costs. We develop stochastic dynamic optimization models of the backup resource-provisioning problem, leading to cost-effective resource-management policies in different practical settings. We present two sets of dynamic provisioning …
Designing Learning Activities For Experiential Learning In A Design Thinking Course, Benjamin Gan, Eng Lieh Ouh
Designing Learning Activities For Experiential Learning In A Design Thinking Course, Benjamin Gan, Eng Lieh Ouh
Research Collection School Of Computing and Information Systems
One experiential learning design challenge is the duration of learning activities. These learning activities take up time and effort for teachers to design and student to perform. Another design challenge is the minimum instructional guidance of these learning activities which potentially impact the learning effectiveness of novice students. In this paper, we describe our findings of applying experiential learning method in a design thinking course with a list of learning activities performed iteratively. Each of the learning activity varies in their duration required and level of instructional guidance. Our survey seeks to find out which of the learning activities are …
The Information Disclosure Trilemma: Privacy, Attribution And Dependency, Ping Fan Ke
The Information Disclosure Trilemma: Privacy, Attribution And Dependency, Ping Fan Ke
Research Collection School Of Computing and Information Systems
Information disclosure has been an important mechanism to increase transparency and welfare in various contexts, from rating a restaurant to whistleblowing the wrongdoing of government agencies. Yet, the author often needs to be sacrificed during information disclosure process – an anonymous disclosure will forgo the reputation and compensation whereas an identifiable disclosure will face the threat of retaliation. On the other hand, the adoption of privacy-enhancing technologies (PETs) lessens the tradeoff between privacy and attribution while introducing dependency and potential threats. This study will develop the desirable design principles and possible threats of an information disclosure system, and discuss how …
Salience-Aware Adaptive Resonance Theory For Large-Scale Sparse Data Clustering, Lei Meng, Ah-Hwee Tan, Chunyan Miao
Salience-Aware Adaptive Resonance Theory For Large-Scale Sparse Data Clustering, Lei Meng, Ah-Hwee Tan, Chunyan Miao
Research Collection School Of Computing and Information Systems
Sparse data is known to pose challenges to cluster analysis, as the similarity between data tends to be ill-posed in the high-dimensional Hilbert space. Solutions in the literature typically extend either k-means or spectral clustering with additional steps on representation learning and/or feature weighting. However, adding these usually introduces new parameters and increases computational cost, thus inevitably lowering the robustness of these algorithms when handling massive ill-represented data. To alleviate these issues, this paper presents a class of self-organizing neural networks, called the salience-aware adaptive resonance theory (SA-ART) model. SA-ART extends Fuzzy ART with measures for cluster-wise salient feature modeling. …
An Iot-Driven Smart Cafe Solution For Human Traffic Management, Maruthi Prithivirajan, Kyong Jin Shim
An Iot-Driven Smart Cafe Solution For Human Traffic Management, Maruthi Prithivirajan, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
In this study, we present an IoT-driven solution for human traffic management in a corporate cafe. Using IoT sensors, our system monitors human traffic in a physical cafe located at a large international corporation located in Singapore. The backend system analyzes the streaming data from the sensors and provides insights useful to the cafe visitors as well as the cafe manager.