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Articles 4111 - 4140 of 9025

Full-Text Articles in Computer Sciences

Mixed Dish Recognition Through Multi-Label Learning, Yunan Wang, Jing-Jing Chen, Chong-Wah Ngo, Tat-Seng Chua, Wanli Zuo, Zhaoyan Ming Jun 2019

Mixed Dish Recognition Through Multi-Label Learning, Yunan Wang, Jing-Jing Chen, Chong-Wah Ngo, Tat-Seng Chua, Wanli Zuo, Zhaoyan Ming

Research Collection School Of Computing and Information Systems

Mix dish recognition, whose goal is to identify each of the dish type presented on one plate, is generally regarded as a difficult problem. The major challenge of this problem is that different dishes presented in one plate may overlap with each other and there may be no clear boundaries among them. Therefore, labeling the bounding box of each dish type is difficult and not necessarily leading to good results. This paper studies the problem from the perspective of multi-label learning. Specially, we propose to perform dish recognition on region level with multiple granularities. For experimental purpose, we collect two …

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Time-Based Payout Ratio For Coordinating Supply And Demand On An On-Demand Service Platform, Jiaru Bai, Kut C. So, Christopher S. Tang, Xiqun Chen, Hai Wang Jun 2019

Time-Based Payout Ratio For Coordinating Supply And Demand On An On-Demand Service Platform, Jiaru Bai, Kut C. So, Christopher S. Tang, Xiqun Chen, Hai Wang

Research Collection School Of Computing and Information Systems

Many on-demand service platforms use a fixed payout ratio (i.e., the percentage of the platform’s revenue that is paid to the providers) regardless of the customer demand and the number of participating providers that tend to vary over time. In this chapter, we examine the implications of time-based payout ratios. To do so, we first present a queueing model with endogenous supply (number of participating providers) and endogenous demand (customer request rate) to model this on-demand service platform. In our model, earnings-sensitive independent providers have heterogeneous reservation price (for work participation) to serve wait-time and price-sensitive customers with heterogeneous valuation …

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Matching Passengers And Drivers With Multiple Objectives In Ride Sharing Markets, Guodong Lyu, Chung Piaw Teo, Wangchi Cheung, Hai Wang Jun 2019

Matching Passengers And Drivers With Multiple Objectives In Ride Sharing Markets, Guodong Lyu, Chung Piaw Teo, Wangchi Cheung, Hai Wang

Research Collection School Of Computing and Information Systems

In many cities in the world, ride sharing companies, such as Uber, Didi, Grab and Lyft, have been able to leverage on Internet-based platforms to conduct online decision making to connect passengers and drivers. These online platforms facilitate the integration of passengers and drivers’ mobility data on smart phones in real-time, which enables a convenient matching between demand and supply in real time. These clear operational advantages have motivated many similar shared service business models in the public transportation arena, and have been a disruptive force to the traditional taxi industry.

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Crowdbc: A Blockchain-Based Decentralized Framework For Crowdsourcing, Ming Li, Jian Weng, Anjia Yang, Wei Lu, Yue Zhang, Lin Hou, Jiannan Liu, Yang Xiang, Robert H. Deng Jun 2019

Crowdbc: A Blockchain-Based Decentralized Framework For Crowdsourcing, Ming Li, Jian Weng, Anjia Yang, Wei Lu, Yue Zhang, Lin Hou, Jiannan Liu, Yang Xiang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Crowdsourcing systems which utilize the human intelligence to solve complex tasks have gained considerable interest and adoption in recent years. However, the majority of existing crowdsourcing systems rely on central servers, which are subject to the weaknesses of traditional trust-based model, such as single point of failure. They are also vulnerable to distributed denial of service (DDoS) and Sybil attacks due to malicious users involvement. In addition, high service fees from the crowdsourcing platform may hinder the development of crowdsourcing. How to address these potential issues has both research and substantial value. In this paper, we conceptualize a blockchain-based decentralized …

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Buscope: Fusing Individual & Aggregated Mobility Behavior For “Live” Smart City Services, Lakmal Buddika Meegahapola, Thivya Kandappu, Kasthuri Jayarajah, Leman Akoglu, Shili Xiang, Archan Misra Jun 2019

Buscope: Fusing Individual & Aggregated Mobility Behavior For “Live” Smart City Services, Lakmal Buddika Meegahapola, Thivya Kandappu, Kasthuri Jayarajah, Leman Akoglu, Shili Xiang, Archan Misra

Research Collection School Of Computing and Information Systems

While analysis of urban commuting data has a long and demonstrated history of providing useful insights into human mobility behavior, such analysis has been performed largely in offline fashion and to aid medium-to-long term urban planning. In this work, we demonstrate the power of applying predictive analytics on real-time mobility data, specifically the smart-card generated trip data of millions of public bus commuters in Singapore, to create two novel and "live" smart city services. The key analytical novelty in our work lies in combining two aspects of urban mobility: (a) conformity: which reflects the predictability in the aggregated flow of …

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An Empirical Study Of Mobile Network Behavior And Application Performance In The Wild, Shiwei Zhang, Weichao Li, Daoyuan Wu, Bo Jin, Rocky K. C. Chang, Debin Gao, Yi Wang, Ricky K. P. Mok Jun 2019

An Empirical Study Of Mobile Network Behavior And Application Performance In The Wild, Shiwei Zhang, Weichao Li, Daoyuan Wu, Bo Jin, Rocky K. C. Chang, Debin Gao, Yi Wang, Ricky K. P. Mok

Research Collection School Of Computing and Information Systems

Monitoring mobile network performance is critical for optimizing the QoE of mobile apps. Until now, few studies have considered the actual network performance that mobile apps experience in a per-app or per-server granularity. In this paper, we analyze a two-year-long dataset collected by a crowdsourcing per-app measurement tool to gain new insights into mobile network behavior and application performance. We observe that only a small portion of WiFi networks can work in high-speed mode, and more than one-third of the observed ISPs still have not deployed 4G networks. For cellular networks, the DNS settings on smartphones can have a significant …

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Metagraph-Based Learning On Heterogeneous Graphs, Yuan Fang, Wenqing Lin, Vincent W. Zheng, Min Wu, Jiaqi Shi, Kevin Chang, Xiao-Li Li Jun 2019

Metagraph-Based Learning On Heterogeneous Graphs, Yuan Fang, Wenqing Lin, Vincent W. Zheng, Min Wu, Jiaqi Shi, Kevin Chang, Xiao-Li Li

Research Collection School Of Computing and Information Systems

Data in the form of graphs are prevalent, ranging from biological and social networks to citation graphs and the Web. Inparticular, most real-world graphs are heterogeneous, containing objects of multiple types, which present new opportunities for manyproblems on graphs. Consider a typical proximity search problem on graphs, which boils down to measuring the proximity between twogiven nodes. Most earlier studies on homogeneous or bipartite graphs only measure a generic form of proximity, without accounting fordifferent “semantic classes”—for instance, on a social network two users can be close for different reasons, such as being classmates orfamily members, which represent two distinct …

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Lpgl: Low-Power Graphics Library For Mobile Ar Headsets, Choi Jaewon, Hyeonjung Park, Jeongyeup Paek, Rajesh Krishna Balan, Jeonggil Ko Jun 2019

Lpgl: Low-Power Graphics Library For Mobile Ar Headsets, Choi Jaewon, Hyeonjung Park, Jeongyeup Paek, Rajesh Krishna Balan, Jeonggil Ko

Research Collection School Of Computing and Information Systems

We present LpGL, an OpenGL API compatible Low-power Graphics Library for energy efficient AR headset applications. We first characterize the power consumption patterns of a state of the art AR headset, Magic Leap One, and empirically show that its internal GPU is the most impactful and controllable energy consumer. Based on the preliminary studies, we design LpGL so that it uses the device's gaze/head orientation information and geometry data to infer user perception information, intercepts application-level graphics API calls, and employs frame rate control, mesh simplification, and culling techniques to enhance energy efficiency of AR headsets without detriment of user …

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Exploratory Analysis Of Individuals' Mobility Patterns And Experienced Conflicts In Workgroup, Nur Camellia Binte Zakaria, Kenneth T. Goh, Youngki Lee, Rajesh Krishna Balan Jun 2019

Exploratory Analysis Of Individuals' Mobility Patterns And Experienced Conflicts In Workgroup, Nur Camellia Binte Zakaria, Kenneth T. Goh, Youngki Lee, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Much research argues the importance of supporting social interactions in teams and communities. The field of mobile sensing alone offers significant advances in recording and understanding human and group behaviours. However, little is known about behavioural changes as a consequence of in-group phenomena. One prominent example is intra-group conflict, which naturally arises between diverse groups of people. We demonstrate the feasibility of our approach to extract mobility patterns of individual's group behaviours sensed from a WiFi indoor localisation system and explore how these patterns relate to their team processes. 62 students enrolled in a project-intensive module, Software Engineering, were tracked …

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Importance Sampling Of Interval Markov Chains, Cyrille Jegourel, Jingyi Wang, Jun Sun Jun 2019

Importance Sampling Of Interval Markov Chains, Cyrille Jegourel, Jingyi Wang, Jun Sun

Research Collection School Of Computing and Information Systems

In real-world systems, rare events often characterize critical situations like the probability that a system fails within some time bound and they are used to model some potentially harmful scenarios in dependability of safety-critical systems. Probabilistic Model Checking has been used to verify dependability properties in various types of systems but is limited by the state space explosion problem. An alternative is the recourse to Statistical Model Checking (SMC) that relies on Monte Carlo simulations and provides estimates within predefined error and confidence bounds. However, rare properties require a large number of simulations before occurring at least once. To tackle …

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Corrn: Cooperative Reflection Removal Network, Renjie Wen, Boxin Shi, Haoliang Li, Ling-Yu Duan, Ah-Hwee Tan, Alex C. Kot Jun 2019

Corrn: Cooperative Reflection Removal Network, Renjie Wen, Boxin Shi, Haoliang Li, Ling-Yu Duan, Ah-Hwee Tan, Alex C. Kot

Research Collection School Of Computing and Information Systems

Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks. Non-learning based methods utilize different handcrafted priors such as the separable sparse gradients caused by different levels of blurs, which often fail due to their limited description capability to the properties of real-world reflections. In this paper, we propose a network with the feature-sharing strategy to tackle this problem in a cooperative and unified framework, by integrating image context information and the multi-scale gradient information. To remove the strong reflections existed in some local regions, we propose a statistic loss by …

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Coordinating Supply And Demand On An On-Demand Service Platform With Impatient Customers, Jiaru Bai, Kut C. So, Christopher S. Tang, Xiqun Chen, Hai Wang Jun 2019

Coordinating Supply And Demand On An On-Demand Service Platform With Impatient Customers, Jiaru Bai, Kut C. So, Christopher S. Tang, Xiqun Chen, Hai Wang

Research Collection School Of Computing and Information Systems

We consider an on-demand service platform using earning sensitive independent providers with heterogeneous reservation price (for work participation) to serve its time and price sensitive customers with heterogeneous valuation of the service. As such, both the supply and demand are "endogenously'' dependent on the price the platform charges its customers and the wage the platform pays its independent providers. We present an analytical model with endogenous supply (number of participating agents) and endogenous demand (customer request rate) to study this on-demand service platform. To coordinate endogenous demand with endogenous supply, we include the steady-state waiting time performance based on a …

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Key-Insulated And Privacy-Preserving Signature Scheme With Publicly Derived Public Key, Zhen Liu, Guomin Yang, Duncan S. Wong, Khoa Nguyen, Huaxiong Wang Jun 2019

Key-Insulated And Privacy-Preserving Signature Scheme With Publicly Derived Public Key, Zhen Liu, Guomin Yang, Duncan S. Wong, Khoa Nguyen, Huaxiong Wang

Research Collection School Of Computing and Information Systems

Since the introduction of Bitcoin in 2008, cryptocurrency has been undergoing a quick and explosive development. At the same time, privacy protection, one of the key merits of cryptocurrency, has attracted much attention by the community. A deterministic wallet algorithm and a stealth address algorithm have been widely adopted in the community, due to their virtues on functionality and privacy protection, which come from a key derivation mechanism that an arbitrary number of derived keys can be generated from a master key. However, these algorithms suffer a vulnerability. In particular, when a minor fault happens (say, one derived key is …

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A Tribute To Robert U. Ayres For A Lifetime Of Work In Technological Forecasting And Related Areas, Steven M. Miller Jun 2019

A Tribute To Robert U. Ayres For A Lifetime Of Work In Technological Forecasting And Related Areas, Steven M. Miller

Research Collection School Of Computing and Information Systems

Bob Ayres was born in the UnitedStates in 1932. For his university studies at the bachelors, masters and PhD levels, he concentrated in physics and mathematics. When we think of Bob today, we think of his pioneering work across the areas of technological forecasting, industrial metabolism and industrial ecology, and the role of energy and thermodynamics in economic growth. How did a person with a strong fundamental education as a physicist end up as a pioneering thinker and thought leader at the intersection of energy, environment and economics?

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Why Is My Code Change Abandoned?, Qingye Wang, Xin Xia, David Lo, Shanping Li Jun 2019

Why Is My Code Change Abandoned?, Qingye Wang, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Software developers contribute numerous changes every day to the code review systems. However, not all submitted changes are merged into a codebase because they might not pass the code review process. Some changes would be abandoned or be asked for resubmission after improvement, which results in more workload for developers and reviewers, and more delays to deliverables. To understand the underlying reasons why changes are abandoned, we conduct an empirical study on the code review of four open source projects (Eclipse, LibreOffice, OpenStack, and Qt).First, we manually analyzed 1459 abandoned changes. Second, we leveraged the open card sorting method to …

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Lightweight Privacy-Preserving Ensemble Classification For Face Recognition, Zhuo Ma, Yang Liu, Ximeng Liu, Jianfeng Ma, Kui Ren Jun 2019

Lightweight Privacy-Preserving Ensemble Classification For Face Recognition, Zhuo Ma, Yang Liu, Ximeng Liu, Jianfeng Ma, Kui Ren

Research Collection School Of Computing and Information Systems

The development of machine learning technology and visual sensors is promoting the wider applications of face recognition into our daily life. However, if the face features in the servers are abused by the adversary, our privacy and wealth can be faced with great threat. Many security experts have pointed out that, by 3-D-printing technology, the adversary can utilize the leaked face feature data to masquerade others and break the E-bank accounts. Therefore, in this paper, we propose a lightweight privacy-preserving adaptive boosting (AdaBoost) classification framework for face recognition (POR) based on the additive secret sharing and edge computing. First, we …

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Geometric Top-K Processing: Updates Since Mdm'16 [Advanced Seminar], Kyriakos Mouratidis Jun 2019

Geometric Top-K Processing: Updates Since Mdm'16 [Advanced Seminar], Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

The top-k query has been studied extensively, and is considered the norm for multi-criteria decision making in large databases. In recent years, research has considered several complementary operators to the traditional top-k query, drawing inspiration (both in terms of problem formulation and solution design) from the geometric nature of the top-k processing model. In this seminar, we will present advances in that stream of work, focusing on updates since the preliminary seminar on the same topic in MDM'16.

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Distributed Similarity Queries In Metric Spaces, Keyu Yang, Xin Ding, Yuanliang Zhang, Lu Chen, Baihua Zheng, Yunjun Gao Jun 2019

Distributed Similarity Queries In Metric Spaces, Keyu Yang, Xin Ding, Yuanliang Zhang, Lu Chen, Baihua Zheng, Yunjun Gao

Research Collection School Of Computing and Information Systems

Similarity queries, including range queries and k nearest neighbor (kNN) queries, in metric spaces have applications in many areas such as multimedia retrieval, computational biology and location-based services. With the growing volumes of data, a distributed method is required. In this paper, we propose an Asynchronous Metric Distributed System (AMDS), to support efficient metric similarity queries in the distributed environment. AMDS uniformly partitions the data with the pivot-mapping technique to ensure the load balancing, and employs publish/subscribe communication model to asynchronous process large scale of queries. The employment of asynchronous processing model also improves robustness and efficiency of AMDS. In …

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Meta-Transfer Learning For Few-Shot Learning, Qianru Sun, Yaoyao Liu, Tat-Seng Chua, Bernt Schiele Jun 2019

Meta-Transfer Learning For Few-Shot Learning, Qianru Sun, Yaoyao Liu, Tat-Seng Chua, Bernt Schiele

Research Collection School Of Computing and Information Systems

Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples only, meta-learning typically uses shallow neural networks (SNNs), thus limiting its effectiveness. In this paper we propose a novel few-shot learning method called meta-transfer learning (MTL) which learns to adapt a deep NN for few shot learning tasks. Specifically, …

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Learning Cross-Modal Embeddings With Adversarial Networks For Cooking Recipes And Food Images, Hao Wang, Doyen Sahoo, Chenghao Liu, Ee-Peng Lim, Steven C. H. Hoi Jun 2019

Learning Cross-Modal Embeddings With Adversarial Networks For Cooking Recipes And Food Images, Hao Wang, Doyen Sahoo, Chenghao Liu, Ee-Peng Lim, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Food computing is playing an increasingly important role in human daily life, and has found tremendous applications in guiding human behavior towards smart food consumption and healthy lifestyle. An important task under the food-computing umbrella is retrieval, which is particularly helpful for health related applications, where we are interested in retrieving important information about food (e.g., ingredients, nutrition, etc.). In this paper, we investigate an open research task of cross-modal retrieval between cooking recipes and food images, and propose a novel framework Adversarial Cross-Modal Embedding (ACME) to resolve the cross-modal retrieval task in food domains. Specifically, the goal is to …

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Collusion Attacks And Fair Time-Locked Deposits For Fast-Payment Transactions In Bitcoin, Xingjie Yu, Shiwen Michael Thang, Yingjiu Li, Robert H. Deng Jun 2019

Collusion Attacks And Fair Time-Locked Deposits For Fast-Payment Transactions In Bitcoin, Xingjie Yu, Shiwen Michael Thang, Yingjiu Li, Robert H. Deng

Research Collection School Of Computing and Information Systems

In Bitcoin network, the distributed storage of multiple copies of the block chain opens up possibilities for doublespending, i.e., a payer issues two separate transactions to two different payees transferring the same coins. While Bitcoin has inherent security mechanism to prevent double-spending attacks, it requires a certain amount of time to detect the doublespending attacks after the transaction has been initiated. Therefore, it is impractical to protect the payees from suffering in double-spending attacks in fast payment scenarios where the time between the exchange of currency and goods or services is shorten to few seconds. Although we cannot prevent double-spending …

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Social Recommendation With Optimal Limited Attention, Xin Wang, Wenwu Zhu, Chenghao Liu Jun 2019

Social Recommendation With Optimal Limited Attention, Xin Wang, Wenwu Zhu, Chenghao Liu

Research Collection School Of Computing and Information Systems

Social recommendation has been playing an important role in suggesting items to users through utilizing information from social connections. However, most existing approaches do not consider the attention factor causing the constraint that people can only accept a limited amount of information due to the limited strength of mind, which has been discovered as an intrinsic physiological property of human by social science. We address this issue by resorting to the concept of limited attention in social science and combining it with machine learning techniques in an elegant way. When introducing the idea of limited attention into social recommendation, two …

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Deep Ecg Estimation Using A Bed-Attached Geophone, Jae Yeon Park, Hyeon Cho, Wonjun Hwang, Rajesh Krishna Balan, Jeong Gil Ko Jun 2019

Deep Ecg Estimation Using A Bed-Attached Geophone, Jae Yeon Park, Hyeon Cho, Wonjun Hwang, Rajesh Krishna Balan, Jeong Gil Ko

Research Collection School Of Computing and Information Systems

Electrocardiogram (ECG) signals offer rich information for analyzing and understanding the cardiac activity of a person. The continuous monitoring of ECG can help diagnose cardiac disorders, such as arrhythmia, effectively. While many wearable healthcare platforms offer continuous ECG monitoring, these devices are cumbersome in the fact that they need to be continuously attached to the human body, which causes uncomfortableness, and limits their usage when monitoring a person's ECG throughout the night as they sleep. In this work, we propose a fully non-intrusive sensing system for monitoring the ECG of a person while in bed. Specifically, we present Heartquake, a …

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A Probabilistic Model Of The Bitcoin Blockchain, Marc Jourdan, Sebastien Blandin, Laura Wynter, Pralhad Deshpande Jun 2019

A Probabilistic Model Of The Bitcoin Blockchain, Marc Jourdan, Sebastien Blandin, Laura Wynter, Pralhad Deshpande

Research Collection School Of Computing and Information Systems

The Bitcoin transaction graph is a public data structure organized as transactions between addresses, each associated with a logical entity. In this work, we introduce a complete probabilistic model of the Bitcoin Blockchain, setting the basis for follow-up AI applications on Bitcoin transactions. We first formulate a set of conditional dependencies induced by the Bitcoin protocol at the block level and derive a corresponding fully observed graphical model of a Bitcoin block. We then extend the model to include hidden entity attributes such as the functional category of the associated logical agent and derive asymptotic bounds on the privacy properties …

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Learning Unsupervised Video Object Segmentation Through Visual Attention, Wenguan Wang, Hongmei Song, Shuyang Zhao, Jianbing Shen, Sanyuan Zhao, Steven C. H. Hoi, Haibin Ling Jun 2019

Learning Unsupervised Video Object Segmentation Through Visual Attention, Wenguan Wang, Hongmei Song, Shuyang Zhao, Jianbing Shen, Sanyuan Zhao, Steven C. H. Hoi, Haibin Ling

Research Collection Yong Pung How School Of Law

This paper conducts a systematic study on the role of visual attention in Unsupervised Video Object Segmentation (UVOS) tasks. By elaborately annotating three popular video segmentation datasets (DAVIS, Youtube-Objects and SegTrack V2) with dynamic eye-tracking data in the UVOS setting, for the first time, we quantitatively verified the high consistency of visual attention behavior among human observers, and found strong correlation between human attention and explicit primary object judgements during dynamic, task-driven viewing. Such novel observations provide an in-depth insight into the underlying rationale behind UVOS. Inspired by these findings, we decouple UVOS into two sub-tasks: UVOS-driven Dynamic Visual Attention …

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Can Algorithms Help Us Decide Who To Trust?, David De Cremer, Jack Mcguire, Yorck Hesselbarth, Ke M Mai Jun 2019

Can Algorithms Help Us Decide Who To Trust?, David De Cremer, Jack Mcguire, Yorck Hesselbarth, Ke M Mai

Research Collection Lee Kong Chian School Of Business

The use of artificial intelligence (AI) and algorithms is increasing within organizations to manage business processes, hire employees, and automate routine organizational decision making. This comes as no surprise, since the application of simple linear algorithms have been shown to outperform human judgment in the accuracy of many administrative tasks. A 2017 Accenture survey also revealed that 85% of executives want to invest more extensively in AI-related technologies over the next three years.

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Salient Object Detection With Pyramid Attention And Salient Edges, Wenguan Wang, Shuyang Zhao, Jianbing Shen, Steven C. H. Hoi, Ali Borji Jun 2019

Salient Object Detection With Pyramid Attention And Salient Edges, Wenguan Wang, Shuyang Zhao, Jianbing Shen, Steven C. H. Hoi, Ali Borji

Research Collection Yong Pung How School Of Law

This paper presents a new method for detecting salient objects in images using convolutional neural networks (CNNs). The proposed network, named PAGE-Net, offers two key contributions. The first is the exploitation of an essential pyramid attention structure for salient object detection. This enables the network to concentrate more on salient regions while considering multi-scale saliency information. Such a stacked attention design provides a powerful tool to efficiently improve the representation ability of the corresponding network layer with an enlarged receptive field. The second contribution lies in the emphasis on the importance of salient edges. Salient edge information offers a strong …

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Stabilized Svrg: Simple Variance Reduction For Nonconvex Optimization, Rong Ge, Zhize Li, Weiyao Wang, Xiang Wang Jun 2019

Stabilized Svrg: Simple Variance Reduction For Nonconvex Optimization, Rong Ge, Zhize Li, Weiyao Wang, Xiang Wang

Research Collection School Of Computing and Information Systems

Variance reduction techniques like SVRG provide simple and fast algorithms for optimizing a convex finite-sum objective. For nonconvex objectives, these techniques can also find a first-order stationary point (with small gradient). However, in nonconvex optimization it is often crucial to find a second-order stationary point (with small gradient and almost PSD hessian). In this paper, we show that Stabilized SVRG (a simple variant of SVRG) can find an $\epsilon$-second-order stationary point using only $\tilde{O}(n^{2/3}/\epsilon^2 + n/\epsilon^{1.5})$ stochastic gradients. To our best knowledge, this is the first second-order guarantee for a simple variant of SVRG. The running time almost matches the …

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Entrans: Leveraging Kinetic Energy Harvesting Signal For Transportation Mode Detection, Guohao Lan, Weitao Xu, Dong Ma, Sara Khalifa, Mahbub Hassan, Wen Hu Jun 2019

Entrans: Leveraging Kinetic Energy Harvesting Signal For Transportation Mode Detection, Guohao Lan, Weitao Xu, Dong Ma, Sara Khalifa, Mahbub Hassan, Wen Hu

Research Collection School Of Computing and Information Systems

Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, as well as providing location-based services. In existing systems, accelerometer and GPS are the dominantly used signal sources for transportation context monitoring which drain out the limited battery life of the wearable devices very quickly. To resolve the high energy consumption issue, in this paper, we present EnTrans, which enables transportation mode detection by using only the kinetic energy harvester as an energy-efficient signal source. The proposed idea is based on the intuition that the vibrations experienced by …

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Automatic Loop Summarization Via Path Dependency Analysis, Xiaofei Xie, Bihuan Chen, Liang Zou, Yang Liu, Wei Le, Xiaohong Li Jun 2019

Automatic Loop Summarization Via Path Dependency Analysis, Xiaofei Xie, Bihuan Chen, Liang Zou, Yang Liu, Wei Le, Xiaohong Li

Research Collection School Of Computing and Information Systems

Analyzing loops is very important for various software engineering tasks such as bug detection, test case generation and program optimization. However, loops are very challenging structures for program analysis, especially when (nested) loops contain multiple paths that have complex interleaving relationships. In this paper, we propose the path dependency automaton (PDA) to capture the dependencies among the multiple paths in a loop. Based on the PDA, we first propose a loop classification to understand the complexity of loop summarization. Then, we propose a loop analysis framework, named Proteus, which takes a loop program and a set of variables of interest …

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