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A Case Study On Automated Fuzz Target Generation For Large Codebases, Matthew Kelly, Christoph Treude, Alex Murray
A Case Study On Automated Fuzz Target Generation For Large Codebases, Matthew Kelly, Christoph Treude, Alex Murray
Research Collection School Of Computing and Information Systems
Fuzz Testing is a largely automated testing technique that provides random and unexpected input to a program in attempt to trigger failure conditions. Much of the research conducted thus far into Fuzz Testing has focused on developing improvements to available Fuzz Testing tools and frameworks in order to improve efficiency. In this paper however, we instead look at a way in which we can reduce the amount of developer time required to integrate Fuzz Testing to help maintain an existing codebase. We accomplish this with a new technique for automatically generating Fuzz Targets, the modified versions of programs on which …
Lightweight Fine-Grained Search Over Encrypted Data In Fog Computing, Yinbin Miao, Jianfeng Ma, Ximeng Liu, Jian Weng, Hongwei Li, Hui Li
Lightweight Fine-Grained Search Over Encrypted Data In Fog Computing, Yinbin Miao, Jianfeng Ma, Ximeng Liu, Jian Weng, Hongwei Li, Hui Li
Research Collection School Of Computing and Information Systems
Fog computing, as an extension of cloud computing, outsources the encrypted sensitive data to multiple fog nodes on the edge of Internet of Things (IoT) to decrease latency and network congestion. However, the existing ciphertext retrieval schemes rarely focus on the fog computing environment and most of them still impose high computational and storage overhead on resource-limited end users. In this paper, we first present a Lightweight Fine-Grained ciphertexts Search (LFGS) system in fog computing by extending Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and Searchable Encryption (SE) technologies, which can achieve fine-grained access control and keyword search simultaneously. The LFGS can shift …
Generating Expensive Relationship Features From Cheap Objects, Xiaogang Wang, Qianru Sun, Tat-Seng Chua, Marcelo Ang
Generating Expensive Relationship Features From Cheap Objects, Xiaogang Wang, Qianru Sun, Tat-Seng Chua, Marcelo Ang
Research Collection School Of Computing and Information Systems
We investigate the problem of object relationship classification of visual scenes. For a relationship object1-predicate-object2 that captures the object interaction, its representation is composed by the combination of object1 and object2 features. As a result, relationship classification models usually bias to the frequent objects, leading to poor generalization to rare or unseen objects. Inspired by the data augmentation methods, we propose a novel Semantic Transform Generative Adversarial Network (ST-GAN) that synthesizes relationship features for rare objects, conditioned on the features from random instances of the objects. Specifically, ST-GAN essentially offers a semantic transform function from cheap object features to expensive …
Efficient Distributed Reachability Querying Of Massive Temporal Graphs, Tianming Zhang, Yunjun Gao, Chen Lu, Wei Guo, Shiliang Pu, Baihua Zheng, Christian S. Jensen
Efficient Distributed Reachability Querying Of Massive Temporal Graphs, Tianming Zhang, Yunjun Gao, Chen Lu, Wei Guo, Shiliang Pu, Baihua Zheng, Christian S. Jensen
Research Collection School Of Computing and Information Systems
Reachability computation is a fundamental graph functionality with a wide range of applications. In spite of this, little work has as yet been done on efficient reachability queries over temporal graphs, which are used extensively to model time-varying networks, such as communication networks, social networks, and transportation schedule networks. Moreover, we are faced with increasingly large real-world temporal networks that may be distributed across multiple data centers. This state of affairs motivates the paper's study of efficient reachability queries on distributed temporal graphs. We propose an efficient index, called Temporal Vertex Labeling (TVL), which is a labeling scheme for distributed …
Foodai: Food Image Recognition Via Deep Learning For Smart Food Logging, Doyen Sahoo, Hao Wang, Ke Shu, Xiongwei Wu, Hung Le, Palakorn Achananuparp, Ee-Peng Lim, Hoi, Steven C. H.
Foodai: Food Image Recognition Via Deep Learning For Smart Food Logging, Doyen Sahoo, Hao Wang, Ke Shu, Xiongwei Wu, Hung Le, Palakorn Achananuparp, Ee-Peng Lim, Hoi, Steven C. H.
Research Collection School Of Computing and Information Systems
An important aspect of health monitoring is effective logging of food consumption. This can help management of diet-related diseases like obesity, diabetes, and even cardiovascular diseases. Moreover, food logging can help fitness enthusiasts, and people who wanting to achieve a target weight. However, food-logging is cumbersome, and requires not only taking additional effort to note down the food item consumed regularly, but also sufficient knowledge of the food item consumed (which is difficult due to the availability of a wide variety of cuisines). With increasing reliance on smart devices, we exploit the convenience offered through the use of smart phones …
Sentence-Level Evidence Embedding For Claim Verification With Hierarchical Attention Networks, Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong
Sentence-Level Evidence Embedding For Claim Verification With Hierarchical Attention Networks, Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Claim verification is generally a task of verifying the veracity of a given claim, which is critical to many downstream applications. It is cumbersome and inefficient for human fact-checkers to find consistent pieces of evidence, from which solid verdict could be inferred against the claim. In this paper, we propose a novel end-to-end hierarchical attention network focusing on learning to represent coherent evidence as well as their semantic relatedness with the claim. Our model consists of three main components: 1) A coherence-based attention layer embeds coherent evidence considering the claim and sentences from relevant articles; 2) An entailment-based attention layer …
Gradient Boosting With Piece-Wise Linear Regression Trees, Yu Shi, Jian Li, Zhize Li
Gradient Boosting With Piece-Wise Linear Regression Trees, Yu Shi, Jian Li, Zhize Li
Research Collection School Of Computing and Information Systems
Gradient Boosted Decision Trees (GBDT) is a very successful ensemble learning algorithm widely used across a variety of applications. Recently, several variants of GBDT training algorithms and implementations have been designed and heavily optimized in some very popular open sourced toolkits including XGBoost, LightGBM and CatBoost. In this paper, we show that both the accuracy and efficiency of GBDT can be further enhanced by using more complex base learners. Specifically, we extend gradient boosting to use piecewise linear regression trees (PL Trees), instead of piecewise constant regression trees, as base learners. We show that PL Trees can accelerate convergence of …
Locating Vulnerabilities In Binaries Via Memory Layout Recovering, Haijun Wang, Xiaofei Xie, Shang-Wei Lin, Yun Lin, Yuekang Li, Shengchao Qin, Yang Liu, Ting Liu
Locating Vulnerabilities In Binaries Via Memory Layout Recovering, Haijun Wang, Xiaofei Xie, Shang-Wei Lin, Yun Lin, Yuekang Li, Shengchao Qin, Yang Liu, Ting Liu
Research Collection School Of Computing and Information Systems
Locating vulnerabilities is an important task for security auditing, exploit writing, and code hardening. However, it is challenging to locate vulnerabilities in binary code, because most program semantics (e.g., boundaries of an array) is missing after compilation. Without program semantics, it is difficult to determine whether a memory access exceeds its valid boundaries in binary code. In this work, we propose an approach to locate vulnerabilities based on memory layout recovery. First, we collect a set of passed executions and one failed execution. Then, for passed and failed executions, we restore their program semantics by recovering fine-grained memory layouts based …
Diffchaser: Detecting Disagreements For Deep Neural Networks, Xiaofei Xie, Lei Ma, Haijun Wang, Yuekang Li, Yang Liu, Xiaohong Li
Diffchaser: Detecting Disagreements For Deep Neural Networks, Xiaofei Xie, Lei Ma, Haijun Wang, Yuekang Li, Yang Liu, Xiaohong Li
Research Collection School Of Computing and Information Systems
The platform migration and customization have become an indispensable process of deep neural network (DNN) development lifecycle. A highprecision but complex DNN trained in the cloud on massive data and powerful GPUs often goes through an optimization phase (e.g., quantization, compression) before deployment to a target device (e.g., mobile device). A test set that effectively uncovers the disagreements of a DNN and its optimized variant provides certain feedback to debug and further enhance the optimization procedure. However, the minor inconsistency between a DNN and its optimized version is often hard to detect and easily bypasses the original test set. This …
Deep Anomaly Detection With Deviation Networks, Guansong Pang, Chunhua Shen, Anton Van Den Hengel
Deep Anomaly Detection With Deviation Networks, Guansong Pang, Chunhua Shen, Anton Van Den Hengel
Research Collection School Of Computing and Information Systems
Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing deep anomaly detection methods, which focus on learning new feature representations to enable downstream anomaly detection methods, perform indirect optimization of anomaly scores, leading to data-inefficient learning and suboptimal anomaly scoring. Also, they are typically designed as unsupervised learning due to the lack of large-scale labeled anomaly data. As a result, they are difficult to leverage prior knowledge (e.g., a few labeled anomalies) when such information is available as in many real-world anomaly detection …
Data-Driven Decision-Support For Process Improvement Through Predictions Of Bed Occupancy Rates, Kar Way Tan, Qi You Ng, Francis Ngoc Hoang Long Nguyen, Sean Shao Wei Lam
Data-Driven Decision-Support For Process Improvement Through Predictions Of Bed Occupancy Rates, Kar Way Tan, Qi You Ng, Francis Ngoc Hoang Long Nguyen, Sean Shao Wei Lam
Research Collection School Of Computing and Information Systems
Managing bed utilization and ensuring the supply keeps up with the demand is not an easy task in a large public hospital with many medical disciplines. The bed managers who makes decisions on reserving and allocating beds centrally require high-dimensional data from several hospital information systems supporting emergency room, specialized clinics and bed management processes. In this work, we put together an automated process for cleaning, consolidating and integrating data from several hospital information systems to several reports required by the bed managers to analyse the bed occupancy situations across more than thirty medical disciplines. To prevent bed crunch situations …
Latent Error Prediction And Fault Localization For Microservice Applications By Learning From System Trace Logs, Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Chao Ji, Dewei Liu, Qilin Xiang, Chuan He
Latent Error Prediction And Fault Localization For Microservice Applications By Learning From System Trace Logs, Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Chao Ji, Dewei Liu, Qilin Xiang, Chuan He
Research Collection School Of Computing and Information Systems
In the production environment, a large part of microservice failures are related to the complex and dynamic interactions and runtime environments, such as those related to multiple instances, environmental configurations, and asynchronous interactions of microservices. Due to the complexity and dynamism of these failures, it is often hard to reproduce and diagnose them in testing environments. It is desirable yet still challenging that these failures can be detected and the faults can be located at runtime of the production environment to allow developers to resolve them efficiently. To address this challenge, in this paper, we propose MEPFL, an approach of …
Improving Law Enforcement Daily Deployment Through Machine Learning-Informed Optimization Under Uncertainty, Jonathan David Chase, Duc Thien Nguyen, Haiyang Sun, Hoong Chuin Lau
Improving Law Enforcement Daily Deployment Through Machine Learning-Informed Optimization Under Uncertainty, Jonathan David Chase, Duc Thien Nguyen, Haiyang Sun, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Urban law enforcement agencies are under great pressure to respond to emergency incidents effectively while operating within restricted budgets. Minutes saved on emergency response times can save lives and catch criminals, and a responsive police force can deter crime and bring peace of mind to citizens. To efficiently minimize the response times of a law enforcement agency operating in a dense urban environment with limited manpower, we consider in this paper the problem of optimizing the spatial and temporal deployment of law enforcement agents to predefined patrol regions in a real-world scenario informed by machine learning. To this end, we …
Knowledge Base Question Answering With Topic Units, Yunshi Lan, Shuohang Wang, Jing Jiang
Knowledge Base Question Answering With Topic Units, Yunshi Lan, Shuohang Wang, Jing Jiang
Research Collection School Of Computing and Information Systems
Knowledge base question answering (KBQA) is an important task in natural language processing. Existing methods for KBQA usually start with entity linking, which considers mostly named entities found in a question as the starting points in the KB to search for answers to the question. However, relying only on entity linking to look for answer candidates may not be sufficient. In this paper, we propose to perform topic unit linking where topic units cover a wider range of units of a KB. We use a generation-and-scoring approach to gradually refine the set of topic units. Furthermore, we use reinforcement learning …
A Survey On Bluetooth 5.0 And Mesh: New Milestones Of Iot, Juenjie Yin, Zheng Yang, Hao Cao, Tongtong Liu, Zimu Zhou, Chenshu Wu
A Survey On Bluetooth 5.0 And Mesh: New Milestones Of Iot, Juenjie Yin, Zheng Yang, Hao Cao, Tongtong Liu, Zimu Zhou, Chenshu Wu
Research Collection School Of Computing and Information Systems
No abstract provided.
Definitions And Mathematical Models Of Single Vehicle Routing Problems With Profits, Pieter Vansteenwegen, Aldy Gunawan
Definitions And Mathematical Models Of Single Vehicle Routing Problems With Profits, Pieter Vansteenwegen, Aldy Gunawan
Research Collection School Of Computing and Information Systems
In this chapter, single vehicle routing problems with profits are introduced anddefined. Three variants are considered: the profitable tour problem, the prizecollecting traveling salesperson problem, and the orienteering problem. The difference between these variants is the way in which the profit and the travel cost, mostlydistance or time, are modeled. Profit and travel cost can be modeled as (part of) theobjective or as a constraint. All three problems differ from the well-known travelingsalesperson problem, for which the only objective is to find the shortest route to visitall customers in a given set. In vehicle routing problems with profits, some customerswill …
Ai-Fashion: Collaborative Ai In The Fashion Industry, Y. Luo, Keng Siau
Ai-Fashion: Collaborative Ai In The Fashion Industry, Y. Luo, Keng Siau
Research Collection School Of Computing and Information Systems
Abstract The word vintage is generally accepted to mean clothing produced in the period between 1920s and 1980s (Cervellon et al., 2012). According to Fischer (2015), fashion usually means rapid changes and up-to-date trendiness. Vintage dressing, however, has been a fashionable trend for over 40 years. Can AI be used to predict the next fashion trend? Fashion industry is currently exploring the use of AI to analyze customer behavior and predict next year’s fashion trends. Predicting the correct next trend is vital to the competitiveness and survivability of fashion brands. Research in this area is not new. For example, research …
Constructing Strong Designated Verifier Signatures From Key Encapsulation Mechanisms, Borui Gong, Ho Man Au, Haiyang Xue
Constructing Strong Designated Verifier Signatures From Key Encapsulation Mechanisms, Borui Gong, Ho Man Au, Haiyang Xue
Research Collection School Of Computing and Information Systems
A designated verifier signature (DVS) allows a signer to convince a verifier that a message has been endorsed in a way that the conviction cannot be transferred to any third party. This is achieved by the property that the signature can be generated by one of them. Since DVS is publicly verifiable, a valid DVS implies that the signature must be created by either the signer or the verifier. To enhance privacy of signers' identity, a strong DVS (SDVS) disallows public verification. In this paper, we investigate various aspects of SDVS with making two contributions. Firstly, we consider SDVS in …
Who Should Make Decision On This Pull Request? Analyzing Time-Decaying Relationships And File Similarities For Integrator Prediction, Jing Jiang, David Lo, Jiateng Zheng, Xin Xia, Yun Yang, Li Zhang
Who Should Make Decision On This Pull Request? Analyzing Time-Decaying Relationships And File Similarities For Integrator Prediction, Jing Jiang, David Lo, Jiateng Zheng, Xin Xia, Yun Yang, Li Zhang
Research Collection School Of Computing and Information Systems
In pull-based development model, integrators are responsible for making decisions about whether to accept pull requests andintegrate code contributions. Ideally, pull requests are assigned to integrators and evaluated within a short time after their submissions. However, the volume of incoming pull requests is large in popular projects, and integrators often encounter difficulties inprocessing pull requests in a timely fashion. Therefore, an automatic integrator prediction approach is required to assign appropriate pull requests to integrators. In this paper, we propose an approach TRFPre which analyzes Time-decaying Relationships andFile similarities to predict integrators. We evaluate the effectiveness of TRFPre on 24 projects …
Ezlog: Data Visualization For Logistics, Aldy Gunawan, Benjamin Gan, Jin An Tan, Sheena L.S.L Villanueva, Timothy K.J. Wen
Ezlog: Data Visualization For Logistics, Aldy Gunawan, Benjamin Gan, Jin An Tan, Sheena L.S.L Villanueva, Timothy K.J. Wen
Research Collection School Of Computing and Information Systems
With the increasing availability of data in the logistics industry due to the digitalization trend, interest and opportunities for leveraging analytics in supply chain management to make data-driven decisions is growing rapidly. In this paper, we introduce EzLog, an integrated visualization prototype platform for supply chain analytics. This web-based platform built by two undergraduate student teams for their capstone course can be used for data wrangling and rapid analysis of data from different business units of a major logistics company. Other functionalities of the system include standard processes to perform data analysis such as supervised extraction, transformation, loading (ETL), data …
Sar: Learning Cross-Language Api Mappings With Little Knowledge, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang
Sar: Learning Cross-Language Api Mappings With Little Knowledge, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
To save effort, developers often translate programs from one programming language to another, instead of implementing it from scratch. Translating application program interfaces (APIs) used in one language to functionally equivalent ones available in another language is an important aspect of program translation. Existing approaches facilitate the translation by automatically identifying the API mappings across programming languages. However, these approaches still require large amount of parallel corpora, ranging from pairs of APIs or code fragments that are functionally equivalent, to similar code comments. To minimize the need of parallel corpora, this paper aims at an automated approach that can map …
Language And Robotics: Complex Sentence Understanding, Seng-Beng Ho, Zhaoxia Wang
Language And Robotics: Complex Sentence Understanding, Seng-Beng Ho, Zhaoxia Wang
Research Collection School Of Computing and Information Systems
Existing robotic systems can take actions based on natural language commands but they tend to be only simple commands. On the other hand, in the domain of Natural Language Processing (NLP), complex sentences are processed, but this NLP domain does not make close contact with robotics. The beginning of computer processing of natural language, when traced back to a system such as Winograd’s SHRUDLU, conceived in 1973, actually aimed to address the issues of Natural Language Understanding (NLU) of relatively complex sentences by a robotic system which in turn takes actions accordingly based on the natural language input. NLU, in …
Cold-Start Aware Deep Memory Networks For Multi-Entity Aspect-Based Sentiment Analysis, Kaisong Song, Wei Gao, Lujun Zhao, Changlong Sun, Xiaozhong Liu
Cold-Start Aware Deep Memory Networks For Multi-Entity Aspect-Based Sentiment Analysis, Kaisong Song, Wei Gao, Lujun Zhao, Changlong Sun, Xiaozhong Liu
Research Collection School Of Computing and Information Systems
Various types of target information have been considered in aspect-based sentiment analysis, such as entities and aspects. Existing research has realized the importance of targets and developed methods with the goal of precisely modeling their contexts via generating target-specific representations. However, all these methods ignore that these representations cannot be learned well due to the lack of sufficient human-annotated target-related reviews, which leads to the data sparsity challenge, a.k.a. cold-start problem here. In this paper, we focus on a more general multiple entity aspect-based sentiment analysis (ME-ABSA) task which aims at identifying the sentiment polarity of different aspects of multiple …
Shared Dynamic Data Audit Supporting Anonymous User Revocation In Cloud Storage, Yinghui Zhang, Chen Chen, Dong Zheng, Rui Guo, Shengmin Xu
Shared Dynamic Data Audit Supporting Anonymous User Revocation In Cloud Storage, Yinghui Zhang, Chen Chen, Dong Zheng, Rui Guo, Shengmin Xu
Research Collection School Of Computing and Information Systems
Collusion between revoked users and cloud service providers can pose a threat to the security of cloud storage data. If the original legitimate users cannot be revoked securely, it will lead to the leakage of shared data, thus affecting the security of cloud storage. In this paper, we combine vector commitment and anonymous revocation of group signature to propose an integrity audit scheme for cloud storage data that can support data modification. The anonymity of the group signature ensures that users’ privacy information will not be snooped by the server. The proposed scheme supports the dynamic operation of stored data …
Deepstellar: Model-Based Quantitative Analysis Of Stateful Deep Learning Systems, Xiaoning Du, Xiaofei Xie, Yi Li, Lei Ma, Yang Liu, Jianjun Zhao
Deepstellar: Model-Based Quantitative Analysis Of Stateful Deep Learning Systems, Xiaoning Du, Xiaofei Xie, Yi Li, Lei Ma, Yang Liu, Jianjun Zhao
Research Collection School Of Computing and Information Systems
Deep Learning (DL) has achieved tremendous success in many cutting-edge applications. However, the state-of-the-art DL systems still suffer from quality issues. While some recent progress has been made on the analysis of feed-forward DL systems, little study has been done on the Recurrent Neural Network (RNN)-based stateful DL systems, which are widely used in audio, natural languages and video processing, etc. In this paper, we initiate the very first step towards the quantitative analysis of RNN-based DL systems. We model RNN as an abstract state transition system to characterize its internal behaviors. Based on the abstract model, we design two …
Towards Robust Resnet: A Small Step But A Giant Leap, Jingfeng Zhang, Bo Han, Laura Wynter, Bryan Kian Hsiang Low, Mohan Kankanhalli
Towards Robust Resnet: A Small Step But A Giant Leap, Jingfeng Zhang, Bo Han, Laura Wynter, Bryan Kian Hsiang Low, Mohan Kankanhalli
Research Collection School Of Computing and Information Systems
This paper presents a simple yet principled approach to boosting the robustness of the residual network (ResNet) that is motivated by a dynamical systems perspective. Namely, a deep neural network can be interpreted using a partial differential equation, which naturally inspires us to characterize ResNet based on an explicit Euler method. This consequently allows us to exploit the step factor h in the Euler method to control the robustness of ResNet in both its training and generalization. In particular, we prove that a small step factor h can benefit its training and generalization robustness during backpropagation and forward propagation, respectively. …
A Secure Iot Cloud Storage System With Fine-Grained Access Control And Decryption Key Exposure Resistance, Shengmin Xu, Guomin Yang, Yi Mu, Ximeng Liu
A Secure Iot Cloud Storage System With Fine-Grained Access Control And Decryption Key Exposure Resistance, Shengmin Xu, Guomin Yang, Yi Mu, Ximeng Liu
Research Collection School Of Computing and Information Systems
Internet of Things (IoT) cloud provides a practical and scalable solution to accommodate the data management in large-scale IoT systems by migrating the data storage and management tasks to cloud service providers (CSPs). However, there also exist many data security and privacy issues that must be well addressed in order to allow the wide adoption of the approach. To protect data confidentiality, attribute-based cryptosystems have been proposed to provide fine-grained access control over encrypted data in loT cloud. Unfortunately, the existing attributed-based solutions are still insufficient in addressing some challenging security problems, especially when dealing with compromised or leaked user …
Who Should Pay The Cost: A Game-Theoretic Model For Government Subsidized Investments To Improve National Cybersecurity, Xinrun Wang, Bo An, Hau Chan
Who Should Pay The Cost: A Game-Theoretic Model For Government Subsidized Investments To Improve National Cybersecurity, Xinrun Wang, Bo An, Hau Chan
Research Collection School Of Computing and Information Systems
Due to the recent cyber attacks, cybersecurity is becoming more critical in modern society. A single attack (e.g., WannaCry ransomware attack) can cause as much as $4 billion in damage. However, the cybersecurity investment by companies is far from satisfactory. Therefore, governments (e.g., in the UK) launch grants and subsidies to help companies to boost their cybersecurity to create a safer national cyber environment. The allocation problem is hard due to limited subsidies and the interdependence between self-interested companies and the presence of a strategic cyber attacker. To tackle the government's allocation problem, we introduce a Stackelberg game-theoretic model where …
Industry 4.0: Ethical And Moral Predicaments, W. Wang, Keng Siau
Industry 4.0: Ethical And Moral Predicaments, W. Wang, Keng Siau
Research Collection School Of Computing and Information Systems
The advancements in software technology and data science are enabling Industry 4.0, aka the Fourth Industrial Revolution or the Industrial Internet of Things (IIoT). While the first three industrial revolutions have brought about immense change, the impact of Industry 4.0 will be much wider and far greater, especially with regard to the easily overlooked ethical and moral aspects. Widening wealth gaps between countries and among classes of people within countries, a potential growing unemployment rate, data privacy and accessibility issues, and the treatment of intelligent agents (e.g., military robots) present new and complex ethical and moral dilemmas. In this article, …
Low-Rank Sparse Subspace For Spectral Clustering, Xiaofeng Zhu, Shichao Zhang, Yonggang Li, Jilian Zhang, Lifeng Yang, Yue Fang
Low-Rank Sparse Subspace For Spectral Clustering, Xiaofeng Zhu, Shichao Zhang, Yonggang Li, Jilian Zhang, Lifeng Yang, Yue Fang
Research Collection School Of Computing and Information Systems
The current two-step clustering methods separately learn the similarity matrix and conduct k means clustering. Moreover, the similarity matrix is learnt from the original data, which usually contain noise. As a consequence, these clustering methods cannot achieve good clustering results. To address these issues, this paper proposes a new graph clustering methods (namely Low-rank Sparse Subspace clustering (LSS)) to simultaneously learn the similarity matrix and conduct the clustering from the low-dimensional feature space of the original data. Specifically, the proposed LSS integrates the learning of similarity matrix of the original feature space, the learning of similarity matrix of the low-dimensional …