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Full-Text Articles in Databases and Information Systems

Proxy-Free Privacy-Preserving Task Matching With Efficient Revocation In Crowdsourcing, Jiangang Shu, Kan Yang, Xiaohua Jia, Ximeng Liu, Cong Wang, Robert H. Deng Jan 2021

Proxy-Free Privacy-Preserving Task Matching With Efficient Revocation In Crowdsourcing, Jiangang Shu, Kan Yang, Xiaohua Jia, Ximeng Liu, Cong Wang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Task matching in crowdsourcing has been extensively explored with the increasing popularity of crowdsourcing. However, privacy of tasks and workers is usually ignored in most of exiting solutions. In this paper, we study the problem of privacy-preserving task matching for crowdsourcing with multiple requesters and multiple workers. Instead of utilizing proxy re-encryption, we propose a proxy-free task matching scheme for multi-requester/multi-worker crowdsourcing, which achieves task-worker matching over encrypted data with scalability and non-interaction. We further design two different mechanisms for worker revocation including ServerLocal Revocation (SLR) and Global Revocation (GR), which realize efficient worker revocation with minimal overhead on the …

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Coherence And Identity Learning For Arbitrary-Length Face Video Generation, Shuquan Ye, Chu Han, Jiaying Lin, Guoqiang Han, Shengfeng He Jan 2021

Coherence And Identity Learning For Arbitrary-Length Face Video Generation, Shuquan Ye, Chu Han, Jiaying Lin, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

Face synthesis is an interesting yet challenging task in computer vision. It is even much harder to generate a portrait video than a single image. In this paper, we propose a novel video generation framework for synthesizing arbitrary-length face videos without any face exemplar or landmark. To overcome the synthesis ambiguity of face video, we propose a divide-and-conquer strategy to separately address the video face synthesis problem from two aspects, face identity synthesis and rearrangement. To this end, we design a cascaded network which contains three components, Identity-aware GAN (IA-GAN), Face Coherence Network, and Interpolation Network. IA-GAN is proposed to …

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Adversarial Specification Mining, Hong Jin Kang, David Lo Jan 2021

Adversarial Specification Mining, Hong Jin Kang, David Lo

Research Collection School Of Computing and Information Systems

There have been numerous studies on mining temporal specifications from execution traces. These approaches learn finite-state automata (FSA) from execution traces when running tests. To learn accurate specifications of a software system, many tests are required. Existing approaches generalize from a limited number of traces or use simple test generation strategies. Unfortunately, these strategies may not exercise uncommon usage patterns of a software system. To address this problem, we propose a new approach, adversarial specification mining, and develop a prototype, DICE (Diversity through Counter-Examples). DICE has two components: DICE-Tester and DICE-Miner. After mining Linear Temporal Logic specifications from an input …

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Do Blockchain And Iot Architecture Create Informedness To Support Provenance Tracking In The Product Lifecycle?, Somnath Mazumdar, Thomas Jensen, Raghava Rao Mukkamala, Robert John Kauffman, Jan Damsgaard Jan 2021

Do Blockchain And Iot Architecture Create Informedness To Support Provenance Tracking In The Product Lifecycle?, Somnath Mazumdar, Thomas Jensen, Raghava Rao Mukkamala, Robert John Kauffman, Jan Damsgaard

Research Collection School Of Computing and Information Systems

Consumers often lack information about the origin and provenance of the products they buy. They may ask: Is a food product truly organic? Or, what is the origin of the gemstone in the ring I purchased? They also may have sustainability concerns about the footprint of a product at the end of its life. Producers and sellers, meanwhile, wish to know how longitudinal tracking of the provenance of products and their components can boost their sales prices and after-market value, and re- veal new business opportunities. We focus on how the product lifecycle (PLC) can be leveraged to track information …

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Enabling Efficient Spatial Keyword Queries On Encrypted Data With Strong Security Guarantees, Xiangyu Wang, Jianfeng Ma, Feng Li, Ximeng Liu, Yinbin Miao, Robert H. Deng Jan 2021

Enabling Efficient Spatial Keyword Queries On Encrypted Data With Strong Security Guarantees, Xiangyu Wang, Jianfeng Ma, Feng Li, Ximeng Liu, Yinbin Miao, Robert H. Deng

Research Collection School Of Computing and Information Systems

Structured Encryption (STE), which allows a server to provide secure search services on encrypted data structures, has been widely investigated in recent years. To meet expressive search requirements in practical applications, a large number of STE constructions have been proposed either on textual keywords or spatial data. However, STE on spatio-textual data, which are widely used in location-based services, has not been fully investigated. In this paper, we formally define the notion of Spatial Keyword Structured Encryption (SKSE) and propose several concrete SKSE constructions with various efficiencysecurity trade-offs. Firstly, we propose a basic construction with linear search complexity, which only …

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Vision-Based Analytics For Improved Ai-Driven Iot Applications, Amit Sharma Dec 2020

Vision-Based Analytics For Improved Ai-Driven Iot Applications, Amit Sharma

Dissertations and Theses Collection (Open Access)

Proliferation of Internet of Things (IoT) sensor systems, primarily driven by cheaper embedded hardware platforms and wide availability of light-weight software platforms, has opened up doors for large-scale data collection opportunities. The availability of massive amount of data has in-turn given way to rapidly growing machine learning models e.g. You Only Look Once (YOLO), Single-Shot-Detectors (SSD) and so on. There has been a growing trend of applying machine learning techniques, e.g., object detection, image classification, face detection etc., on data collected from camera sensors and therefore enabling plethora of vision-sensing applications namely self-driving cars, automatic crowd monitoring, traffic-flow analysis, occupancy …

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Jointly Optimizing Sensing Pipelines For Multimodal Mixed Reality Interaction, Ramesh Darshana Rathnayake Kanatta Gamage, Ashen De Silva, Dasun Puwakdandawa, Lakmal Meegahapola, Archan Misra, Indika Perera Dec 2020

Jointly Optimizing Sensing Pipelines For Multimodal Mixed Reality Interaction, Ramesh Darshana Rathnayake Kanatta Gamage, Ashen De Silva, Dasun Puwakdandawa, Lakmal Meegahapola, Archan Misra, Indika Perera

Research Collection School Of Computing and Information Systems

Natural human interactions for Mixed Reality Applications are overwhelmingly multimodal: humans communicate intent and instructions via a combination of visual, aural and gestural cues. However, supporting low-latency and accurate comprehension of such multimodal instructions (MMI), on resource-constrained wearable devices, remains an open challenge, especially as the state-of-the-art comprehension techniques for each individual modality increasingly utilize complex Deep Neural Network models. We demonstrate the possibility of overcoming the core limitation of latency–vs.–accuracy tradeoff by exploiting cross-modal dependencies–i.e., by compensating for the inferior performance of one model with an increased accuracy of more complex model of a different modality. We present a …

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Co-Embedding Attributed Networks With External Knowledge, Pei-Chi Lo, Ee Peng Lim Dec 2020

Co-Embedding Attributed Networks With External Knowledge, Pei-Chi Lo, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Attributed network embedding aims to learn representations of nodes and their attributes in a low-dimensional space that preserves their semantics. The existing embedding models, however, consider node connectivity and node attributes only while ignoring external knowledge that can enhance node representations for downstream applications. In this paper, we propose a set of new VAE-based embedding models called External Knowledge-Aware Co-Embedding Attributed Network (ECAN) Embeddings to incorporate associations among attributes from relevant external knowledge. Such external knowledge can be extracted from text corpus and knowledge graphs. We use multi-VAE structures to model the attribute associations. To cope with joint encoding of …

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A Hybrid Approach For Detecting Prerequisite Relations In Multi-Modal Food Recipes, Liangming Pan, Jingjing Chen, Shaoteng Liu, Chong-Wah Ngo, Min-Yen Kan, Tat-Seng Chua Dec 2020

A Hybrid Approach For Detecting Prerequisite Relations In Multi-Modal Food Recipes, Liangming Pan, Jingjing Chen, Shaoteng Liu, Chong-Wah Ngo, Min-Yen Kan, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Modeling the structure of culinary recipes is the core of recipe representation learning. Current approaches mostly focus on extracting the workflow graph from recipes based on text descriptions. Process images, which constitute an important part of cooking recipes, has rarely been investigated in recipe structure modeling. We study this recipe structure problem from a multi-modal learning perspective, by proposing a prerequisite tree to represent recipes with cooking images at a step-level granularity. We propose a simple-yet-effective two-stage framework to automatically construct the prerequisite tree for a recipe by (1) utilizing a trained classifier to detect pairwise prerequisite relations that fuses …

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Security Analysis Of Permission Re-Delegation Vulnerabilities In Android Apps, Biniam Fisseha Demissie, Mariano Ceccato, Lwin Khin Shar Dec 2020

Security Analysis Of Permission Re-Delegation Vulnerabilities In Android Apps, Biniam Fisseha Demissie, Mariano Ceccato, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

The Android platform facilitates reuse of app functionalities by allowing an app to request an action from another app through inter-process communication mechanism. This feature is one of the reasons for the popularity of Android, but it also poses security risks to the end users because malicious, unprivileged apps could exploit this feature to make privileged apps perform privileged actions on behalf of them. In this paper, we investigate the hybrid use of program analysis, genetic algorithm based test generation, natural language processing, machine learning techniques for precise detection of permission re-delegation vulnerabilities in Android apps. Our approach first groups …

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Nearest Centroid: A Bridge Between Statistics And Machine Learning, Manoj Thulasidas Dec 2020

Nearest Centroid: A Bridge Between Statistics And Machine Learning, Manoj Thulasidas

Research Collection School Of Computing and Information Systems

In order to guide our students of machine learning in their statistical thinking, we need conceptually simple and mathematically defensible algorithms. In this paper, we present the Nearest Centroid algorithm (NC) algorithm as a pedagogical tool, combining the key concepts behind two foundational algorithms: K-Means clustering and K Nearest Neighbors (k- NN). In NC, we use the centroid (as defined in the K-Means algorithm) of the observations belonging to each class in our training data set and its distance from a new observation (similar to k-NN) for class prediction. Using this obvious extension, we will illustrate how the concepts of …

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Secure Answer Book And Automatic Grading, Manoj Thulasidas Dec 2020

Secure Answer Book And Automatic Grading, Manoj Thulasidas

Research Collection School Of Computing and Information Systems

In response to the growing need to perform assessments online, we have developed a secure answer book, as well as a tool for automatically grading it for our course on spread- sheet modeling. We applied these techniques to a cohort of about 160 students who took the course last term. In this paper, we describe the design, implementation and the techniques employed to enhance both the security of the answer book and the ease, accuracy and consistency of grading. In addition, we summarize the experience and takeaways, both from the instructor and the student perspectives. Although the answer book and …

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Causal Intervention For Weakly-Supervised Semantic Segmentation, Zhang Dong, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, Qianru Sun Dec 2020

Causal Intervention For Weakly-Supervised Semantic Segmentation, Zhang Dong, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, Qianru Sun

Research Collection School Of Computing and Information Systems

We present a causal inference framework to improve Weakly-Supervised Semantic Segmentation (WSSS). Specifically, we aim to generate better pixel-level pseudo-masks by using only image-level labels --- the most crucial step in WSSS. We attribute the cause of the ambiguous boundaries of pseudo-masks to the confounding context, e.g., the correct image-level classification of "horse'' and "person'' may be not only due to the recognition of each instance, but also their co-occurrence context, making the model inspection (e.g., CAM) hard to distinguish between the boundaries. Inspired by this, we propose a structural causal model to analyze the causalities among images, contexts, and …

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Debunking Rumors On Twitter With Tree Transformer, Jing Ma, Wei Gao Dec 2020

Debunking Rumors On Twitter With Tree Transformer, Jing Ma, Wei Gao

Research Collection School Of Computing and Information Systems

Rumors are manufactured with no respect for accuracy, but can circulate quickly and widely by "word-of-post" through social media conversations. Conversation tree encodes important information indicative of the credibility of rumor. Existing conversation-based techniques for rumor detection either just strictly follow tree edges or treat all the posts fully-connected during feature learning. In this paper, we propose a novel detection model based on tree transformer to better utilize user interactions in the dialogue where post-level self-attention plays the key role for aggregating the intra-/inter-subtree stances. Experimental results on the TWITTER and PHEME datasets show that the proposed approach consistently improves …

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A Bert-Based Dual Embedding Model For Chinese Idiom Prediction, Minghuan Tan, Jing Jiang Dec 2020

A Bert-Based Dual Embedding Model For Chinese Idiom Prediction, Minghuan Tan, Jing Jiang

Research Collection School Of Computing and Information Systems

Chinese idioms are special fixed phrases usually derived from ancient stories, whose meanings are oftentimes highly idiomatic and non-compositional. The Chinese idiom prediction task is to select the correct idiom from a set of candidate idioms given a context with a blank. We propose a BERT-based dual embedding model to encode the contextual words as well as to learn dual embeddings of the idioms. Specifically, we first match the embedding of each candidate idiom with the hidden representation corresponding to the blank in the context. We then match the embedding of each candidate idiom with the hidden representations of all …

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Learning To Dispatch For Job Shop Scheduling Via Deep Reinforcement Learning, Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, Xu Chi Dec 2020

Learning To Dispatch For Job Shop Scheduling Via Deep Reinforcement Learning, Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, Xu Chi

Research Collection School Of Computing and Information Systems

Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad of specialized knowledge and often delivering limited performance. In this paper, we propose to automatically learn PDRs via an end-to-end deep reinforcement learning agent. We exploit the disjunctive graph representation of JSSP, and propose a Graph Neural Network based scheme to embed the states encountered during solving. The resulting policy network is size-agnostic, effectively enabling generalization on large-scale instances. Experiments show that the agent can learn high-quality PDRs from scratch with elementary raw …

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Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam In Deep Learning, Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven C. H. Hoi, Weinan E Dec 2020

Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam In Deep Learning, Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven C. H. Hoi, Weinan E

Research Collection School Of Computing and Information Systems

It is not clear yet why ADAM-alike adaptive gradient algorithms suffer from worse generalization performance than SGD despite their faster training speed. This work aims to provide understandings on this generalization gap by analyzing their local convergence behaviors. Specifically, we observe the heavy tails of gradient noise in these algorithms. This motivates us to analyze these algorithms through their Lévy-driven stochastic differential equations (SDEs) because of the similar convergence behaviors of an algorithm and its SDE. Then we establish the escaping time of these SDEs from a local basin. The result shows that (1) the escaping time of both SGD …

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Blockchain-Based Public Auditing And Secure Deduplication With Fair Arbitration, Haoran Yuan, Xiaofeng Chen, Jianfeng Wang, Jiaming Yuan, Hongyang Yan, Willy Susilo Dec 2020

Blockchain-Based Public Auditing And Secure Deduplication With Fair Arbitration, Haoran Yuan, Xiaofeng Chen, Jianfeng Wang, Jiaming Yuan, Hongyang Yan, Willy Susilo

Research Collection School Of Computing and Information Systems

Data auditing enables data owners to verify the integrity of their sensitive data stored at an untrusted cloud without retrieving them. This feature has been widely adopted by commercial cloud storage. However, the existing approaches still have some drawbacks. On the one hand, the existing schemes have a defect of fair arbitration, i.e., existing auditing schemes lack an effective method to punish the malicious cloud service provider (CSP) and compensate users whose data integrity is destroyed. On the other hand, a CSP may store redundant and repetitive data. These redundant data inevitably increase management overhead and computational cost during the …

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Business Practice Of Social Media - Platform And Customer Service Adoption, Shujing Sun, Yang Gao, Huaxia Rui Dec 2020

Business Practice Of Social Media - Platform And Customer Service Adoption, Shujing Sun, Yang Gao, Huaxia Rui

Research Collection School Of Computing and Information Systems

This paper examines the key drivers in business adoptions of the platform and customer service within the context of social media. We carry out the empirical analyses using the decision trajectories of the international airline industry on Twitter. We find that a firm's decision-making is subject to both peer influence and consumer pressure. Regarding peer influence, we find that the odds of both adoptions increase when the extent of peers' adoption increases. We also identify the distinctive role of consumers. Specifically, before the platform adoption, firms learn about potential consequences from consumer reactions to peers' adoptions. Upon the platform adoption, …

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A Near-Optimal Change-Detection Based Algorithm For Piecewise-Stationary Combinatorial Semi-Bandits, Huozhi Zhou, Lingda Wang, Lav N. Varshney, Ee-Peng Lim Dec 2020

A Near-Optimal Change-Detection Based Algorithm For Piecewise-Stationary Combinatorial Semi-Bandits, Huozhi Zhou, Lingda Wang, Lav N. Varshney, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

We investigate the piecewise-stationary combinatorial semi-bandit problem. Compared to the original combinatorial semi-bandit problem, our setting assumes the reward distributions of base arms may change in a piecewise-stationary manner at unknown time steps. We propose an algorithm, GLR-CUCB, which incorporates an efficient combinatorial semi-bandit algorithm, CUCB, with an almost parameter-free change-point detector, the Generalized Likelihood Ratio Test (GLRT). Our analysis shows that the regret of GLR-CUCB is upper bounded by O(√NKT logT), where N is the number of piecewise-stationary segments, K is the number of base arms, and T is the number of time steps. As a complement, we also …

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A Social Network Analysis Of Jobs And Skills, Derrick Ming Yang Lee, Dion Wei Xuan Ang, Grace Mei Ching Pua, Lee Ning Ng, Sharon Purbowo, Eugene Wen Jia Choy, Kyong Jin Shim Dec 2020

A Social Network Analysis Of Jobs And Skills, Derrick Ming Yang Lee, Dion Wei Xuan Ang, Grace Mei Ching Pua, Lee Ning Ng, Sharon Purbowo, Eugene Wen Jia Choy, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In this study, we analyzed job roles and skills across industries in Singapore. Using social network analysis, we identified job roles with similar required skills, and we also identified relationships between job skills. Our analysis visualizes such relationships in an intuitive way. Insights derived from our analyses are expected to assist job seekers, employers as well as recruitment agencies wanting to understand trending and required job roles and skills in today’s fast changing world.

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Social Media Analytics: A Case Study Of Singapore General Election 2020, Sebastian Zhi Tao Khoo, Leong Hock Ho, Ee Hong Lee, Danston Kheng Boon Goh, Zehao Zhang, Swee Hong Ng, Haodi Qi, Kyong Jin Shim Dec 2020

Social Media Analytics: A Case Study Of Singapore General Election 2020, Sebastian Zhi Tao Khoo, Leong Hock Ho, Ee Hong Lee, Danston Kheng Boon Goh, Zehao Zhang, Swee Hong Ng, Haodi Qi, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

The 2020 Singaporean General Election (GE2020) was a general election held in Singapore on July 10, 2020. In this study, we present an analysis on social conversations about GE2020 during the election period. We analyzed social conversations from popular platforms such as Twitter, HardwareZone, and TR Emeritus.

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Digital Social Listening On Conversations About Sexual Harassment, Xuesi Sim, Ern Rae Chang, Yu Xiang Ong, Jie Ying Yeo, Christine Bai Shuang Yan, Eugene Wen Jia Choy, Kyong Jin Shim Dec 2020

Digital Social Listening On Conversations About Sexual Harassment, Xuesi Sim, Ern Rae Chang, Yu Xiang Ong, Jie Ying Yeo, Christine Bai Shuang Yan, Eugene Wen Jia Choy, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In light of the #MeToo movement and publicized sexual harassment incidents in Singapore in recent years, we built an analytics pipeline for performing digital social listening on conversations about sexual harassment for AWARE (Association of Women for Action and Research). Our social network analysis results identified key influencers that AWARE can engage for sexual harassment awareness campaigns. Further, our analysis results suggest new hashtags that AWARE can use to run social media campaigns and achieve greater reach.

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Differential Privacy Protection Over Deep Learning: An Investigation Of Its Impacted Factors, Ying Lin, Ling-Yan Bao, Ze-Minghui Li, Shu-Sheng Si, Chao-Hsien Chu Dec 2020

Differential Privacy Protection Over Deep Learning: An Investigation Of Its Impacted Factors, Ying Lin, Ling-Yan Bao, Ze-Minghui Li, Shu-Sheng Si, Chao-Hsien Chu

Research Collection School Of Computing and Information Systems

Deep learning (DL) has been widely applied to achieve promising results in many fields, but it still exists various privacy concerns and issues. Applying differential privacy (DP) to DL models is an effective way to ensure privacy-preserving training and classification. In this paper, we revisit the DP stochastic gradient descent (DP-SGD) method, which has been used by several algorithms and systems and achieved good privacy protection. However, several factors, such as the sequence of adding noise, the models used etc., may impact its performance with various degrees. We empirically show that adding noise first and clipping second will not only …

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Interventional Few-Shot Learning, Zhongqi Yue, Zhang Hanwang, Qianru Sun, Xian-Sheng Hua Dec 2020

Interventional Few-Shot Learning, Zhongqi Yue, Zhang Hanwang, Qianru Sun, Xian-Sheng Hua

Research Collection School Of Computing and Information Systems

We uncover an ever-overlooked deficiency in the prevailing Few-Shot Learning (FSL) methods: the pre-trained knowledge is indeed a confounder that limits the performance. This finding is rooted from our causal assumption: a Structural Causal Model (SCM) for the causalities among the pre-trained knowledge, sample features, and labels. Thanks to it, we propose a novel FSL paradigm: Interventional Few-Shot Learning (IFSL). Specifically, we develop three effective IFSL algorithmic implementations based on the backdoor adjustment, which is essentially a causal intervention towards the SCM of many-shot learning: the upper-bound of FSL in a causal view. It is worth noting that the contribution …

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Heterogeneous Univariate Outlier Ensembles In Multidimensional Data, Guansong Pang, Longbing Cao Dec 2020

Heterogeneous Univariate Outlier Ensembles In Multidimensional Data, Guansong Pang, Longbing Cao

Research Collection School Of Computing and Information Systems

In outlier detection, recent major research has shifted from developing univariate methods to multivariate methods due to the rapid growth of multidimensional data. However, one typical issue of this paradigm shift is that many multidimensional data often mainly contains univariate outliers, in which many features are actually irrelevant. In such cases, multivariate methods are ineffective in identifying such outliers due to the potential biases and the curse of dimensionality brought by irrelevant features. Those univariate outliers might be well detected by applying univariate outlier detectors in individually relevant features. However, it is very challenging to choose a right univariate detector …

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Design Of A Two-Echelon Freight Distribution System In An Urban Area Considering Third-Party Logistics And Loading-Unloading Zones, Vincent F. Yu, Winarno, Shih-Wei Lin, Aldy Gunawan Dec 2020

Design Of A Two-Echelon Freight Distribution System In An Urban Area Considering Third-Party Logistics And Loading-Unloading Zones, Vincent F. Yu, Winarno, Shih-Wei Lin, Aldy Gunawan

Research Collection School Of Computing and Information Systems

This research examines the problem of designing a two-echelon freight distribution system in a dense urban area that considers third-party logistics (TPL) and loading–unloading zones (LUZs). The proposed system takes advantage of outsourcing the last mile deliveries to a TPL provider and utilizing LUZs as temporary intermediate facilities instead of using permanent intermediate facilities to consolidate freight. A mathematical model and a simulated annealing (SA) algorithm are developed to solve the problem. The efficiency and effectiveness of the proposed SA heuristic are verified by testing it on existing benchmark instances. Computational results show that the performance of the proposed SA …

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Sharper Generalisation Bounds For Pairwise Learning, Yunwen Lei, Antoine Ledent, Marius Kloft Dec 2020

Sharper Generalisation Bounds For Pairwise Learning, Yunwen Lei, Antoine Ledent, Marius Kloft

Research Collection School Of Computing and Information Systems

Pairwise learning refers to learning tasks with loss functions depending on a pair of training examples, which includes ranking and metric learning as specific examples. Recently, there has been an increasing amount of attention on the generalization analysis of pairwise learning to understand its practical behavior. However, the existing stability analysis provides suboptimal high-probability generalization bounds. In this paper, we provide a refined stability analysis by developing generalization bounds which can be √nn-times faster than the existing results, where nn is the sample size. This implies excess risk bounds of the order O(n−1/2) (up to a logarithmic factor) for both …

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Deep Multi-Task Learning For Depression Detection And Prediction In Longitudinal Data, Guansong Pang, Ngoc Thien Anh Pham, Emma Baker, Rebecca Bentley, Anton Van Den Hengel Dec 2020

Deep Multi-Task Learning For Depression Detection And Prediction In Longitudinal Data, Guansong Pang, Ngoc Thien Anh Pham, Emma Baker, Rebecca Bentley, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. However, they are challenged by the relative scarcity of instances of depression in the data. In this work we introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks, namely one-class metric learning and anomaly ranking. The auxiliary tasks introduce an inductive bias that improves the classification model's generalizability on small depression …

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Cost-Sensitive Deep Forest For Price Prediction, Chao Ma, Zhenbing Liu, Zhiguang Cao, Wen Song, Jie Zhang, Weiliang Zeng Nov 2020

Cost-Sensitive Deep Forest For Price Prediction, Chao Ma, Zhenbing Liu, Zhiguang Cao, Wen Song, Jie Zhang, Weiliang Zeng

Research Collection School Of Computing and Information Systems

For many real-world applications, predicting a price range is more practical and desirable than predicting a concrete value. In this case, price prediction can be regarded as a classification problem. Although deep forest is recognized as the best solution to many classification problems, a crucial issue limits its direct application to price prediction, i.e., it treated all the misclassifications equally no matter how far away they are from the real classes, since their impacts on the accuracy are the same. This is unreasonable to price prediction as the misclassification should be as close to the real price range as possible …

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