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Articles 4051 - 4080 of 9025

Full-Text Articles in Computer Sciences

Chatbots: Conversation Killers Or Makers?, Jing Jiang Jul 2019

Chatbots: Conversation Killers Or Makers?, Jing Jiang

MITB Thought Leadership Series

Whether you’re aware of it or not, the chances are you’ve been chatting to robots of late. While these bots are faceless and unseen, don’t be fooled into thinking they aren’t there. In fact, chatbots, have been around since the 1960s at least, but with the progress in artificial intelligence, cloud computing and voice recognition, they’ve received both a functionality and a popularity boost. From the cosmetic to the life-changing, nowadays, chatbots can do anything from helping a person lose weight to assisting refugees applying for asylum.

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Lightweight And Efficient Neural Natural Language Processing With Quaternion Networks, Yi Tay, Aston Zhang, Anh Tuan Luu, Jinfeng Rao, Shuai Zhang, Shuohang Wang, Jie Fu, Siu Cheung Hui Jul 2019

Lightweight And Efficient Neural Natural Language Processing With Quaternion Networks, Yi Tay, Aston Zhang, Anh Tuan Luu, Jinfeng Rao, Shuai Zhang, Shuohang Wang, Jie Fu, Siu Cheung Hui

SCIS Student Publications

Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient. This paper proposes a series of lightweight and memory efficient neural architectures for a potpourri of natural language processing (NLP) tasks. To this end, our models exploit computation using Quaternion algebra and hypercomplex spaces, enabling not only expressive inter-component interactions but also significantly (75%) reduced parameter size due to lesser degrees of freedom in the Hamilton product. We propose Quaternion variants of models, giving rise to new architectures such as the Quaternion attention Model and Quaternion Transformer. Extensive experiments on a battery of NLP tasks demonstrates the …

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Simple And Effective Curriculum Pointer-Generator Networks For Reading Comprehension Over Long Narratives, Yi Tay, Shuohang Wang, Anh Tuan Luu, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, Aston Zhang Jul 2019

Simple And Effective Curriculum Pointer-Generator Networks For Reading Comprehension Over Long Narratives, Yi Tay, Shuohang Wang, Anh Tuan Luu, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, Aston Zhang

SCIS Student Publications

This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens. We propose a curriculum learning (CL) based Pointer-Generator framework for reading/sampling over large documents, enabling diverse training of the neural model based on the notion of alternating contextual difficulty. This can be interpreted as a form of domain randomization and/or generative pretraining during training. To this end, the usage of the Pointer-Generator softens the requirement of having the answer within the context, enabling us to construct diverse training samples for learning. Additionally, we propose a new Introspective Alignment Layer (IAL), which …

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Searching Activity Trajectories With Semantics, Li-Hua Yin, Huiwen Liu Jul 2019

Searching Activity Trajectories With Semantics, Li-Hua Yin, Huiwen Liu

PhD Student’s Publications Collection

With the widespread use of smart phones and mobile Internet, social network users have generated massive geo-tagged tweets, photos and videos to form lots of informative trajectories which reveal not only their spatio-temporal dynamics, but also their activities in the physical world. Existing spatial trajectory query studies mainly focus on analyzing the spatio-temporal properties of the users’ trajectories, while leaving the understanding of their activities largely untouched. In this paper, we incorporate the semantics of the activity information embedded in trajectories into query modelling and processing, with the aim of providing end users more informative and meaningful results. To this …

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Sensitive Behavior Analysis Of Android Applications On Unrooted Devices In The Wild, Xiaoxiao Tang Jul 2019

Sensitive Behavior Analysis Of Android Applications On Unrooted Devices In The Wild, Xiaoxiao Tang

Dissertations and Theses Collection (Open Access)

Dynamic analysis is widely used in malware detection, taint analysis, vulnerability detection, and other areas for enhancing the security of Android. Compared to static analysis, dynamic analysis is immune to common code obfuscation techniques and dynamic code loading. Existing dynamic analysis techniques rely on in-lab running environment (e.g., modified systems, rooted devices, or emulators) and require automatic input generators to execute the target app. However, these techniques could be bypassed by anti-analysis techniques that allow apps to hide sensitive behavior when an in-lab environment is detected through predefined heuristics (e.g., IMEI number of the device is invalid). Meanwhile, current input …

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Exploiting Mobility For Predictive Urban Analytics & Operations, Kasthuri Jayarajah Jul 2019

Exploiting Mobility For Predictive Urban Analytics & Operations, Kasthuri Jayarajah

Dissertations and Theses Collection (Open Access)

As cities worldwide invest heavily in smart city infrastructure, it invites opportunities for a next wave of urban analytics. Unlike its predecessors, urban analytics applications and services can now be real-time and proactive -- they can (a) leverage situational data from large deployments of connected sensors, (b) capture attributes of a variety of entities that make up the urban fabric (e.g., people and their social relationships, transport nodes, utilities, etc.), and (c) use predictive insights to both proactively optimize urban operations (e.g., HVAC systems in smart buildings, buses in the transportation network, crowd-workers, etc.) and promote smarter policy decisions (e.g., …

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Making Sense Of Crowd-Generated Content In Domain-Specific Settings, Agus Sulistya Jul 2019

Making Sense Of Crowd-Generated Content In Domain-Specific Settings, Agus Sulistya

Dissertations and Theses Collection (Open Access)

The rapid advances of the Web have changed the ways information is distributed and exchanged among individuals and organizations. Various content from different domains are generated daily and contributed by users' daily activities, such as posting messages in a microblog platform, or collaborating in a question and answer site. To deal with such tremendous volume of user generated content, there is a need for approaches that are able to handle the mass amount of available data and to extract knowledge hidden in the user generated content. This dissertation attempts to make sense of the generated content to help in three …

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Stressmon: Large Scale Detection Of Stress And Depression In Campus Environment Using Passive Coarse-Grained Location Data, Camellia Zakaria Jul 2019

Stressmon: Large Scale Detection Of Stress And Depression In Campus Environment Using Passive Coarse-Grained Location Data, Camellia Zakaria

Dissertations and Theses Collection (Open Access)

The rising mental health illnesses of severe stress and depression is of increasing concern worldwide. Often associated by similarities in symptoms, severe stress can take a toll on a person’s productivity and result in depression if the stress is left unmanaged. Unfortunately, depression can occur without any feelings of stress. With depression growing as a leading cause of disability in economic productivity, there has been a sharp rise in mental health initiatives to improve stress and depression management. To offer such services conveniently and discreetly, recent efforts have focused on using mobile technologies. However, these initiatives usually require users to …

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Volumetric Optimization Of Freight Cargo Loading: Case Study Of A Smu Forwarder, Tristan Lim, Michael Ser Chong Ping, Mark Goh, Shi Ying Jacelyn Tan Jul 2019

Volumetric Optimization Of Freight Cargo Loading: Case Study Of A Smu Forwarder, Tristan Lim, Michael Ser Chong Ping, Mark Goh, Shi Ying Jacelyn Tan

Research Collection School Of Computing and Information Systems

Purpose: Freight forwarders faces a challenging environment of high market volatility and margin compression risks. Hence, strategic consideration is given to undertaking capacity management and transport asset ownership to achieve longer term cost leadership. Doing so will also help to address management issues, such as better control of potential transport disruptions, improve scheduling flexibility and efficiency, and provide service level enhancement.Design/methodology/approach: The case company currently hastruck resource which is unprofitable, and the firm’s schedulers are having difficulty optimizing the loading capacity. We apply Genetic Algorithm (GA) to undertake volumetric optimization of truckcapacity and to build an easy-to-use platform to help …

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A Review On Swarm Intelligence And Evolutionary Algorithms For Solving Flexible Job Shop Scheduling Problems, Kaizhou Gao, Zhiguang Cao, Le Zhang, Zhenghua Chen, Yuyan Han, Quanke Pan Jul 2019

A Review On Swarm Intelligence And Evolutionary Algorithms For Solving Flexible Job Shop Scheduling Problems, Kaizhou Gao, Zhiguang Cao, Le Zhang, Zhenghua Chen, Yuyan Han, Quanke Pan

Research Collection School Of Computing and Information Systems

Flexible job shop scheduling problems (FJSP) have received much attention from academia and industry for many years. Due to their exponential complexity, swarm intelligence (SI) and evolutionary algorithms (EA) are developed, employed and improved for solving them. More than 60% of the publications are related to SI and EA. This paper intents to give a comprehensive literature review of SI and EA for solving FJSP. First, the mathematical model of FJSP is presented and the constraints in applications are summarized. Then, the encoding and decoding strategies for connecting the problem and algorithms are reviewed. The strategies for initializing algorithms? population …

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Applying Case-Based Learning For A Postgraduate Software Architecture Course, Eng Lieh Ouh, Yunghans Irawan Jul 2019

Applying Case-Based Learning For A Postgraduate Software Architecture Course, Eng Lieh Ouh, Yunghans Irawan

Research Collection School Of Computing and Information Systems

Software architecture remains a difficult subject for learners to grasp and for educators to teach given its level of abstraction. On the other hand, case-based learning (CBL) is a popular teaching approach used across disciplines especially in business, medicine and law where students work in groups apply their knowledge to solve real-world case studies, or scenarios using their reasoning skills and existing theoretical knowledge. In this paper, we provide how we apply case-based learning to address the challenge in teaching a postgraduate software architecture course. Our learners are postgraduate students taking a master’s program in software engineering. We first describe …

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Decentralizing Air Traffic Flow Management With Blockchain Based Reinforcement Learning, Nguyen Binh Duong Ta, Umang Chaudhary, Hong-Linh Truong Jul 2019

Decentralizing Air Traffic Flow Management With Blockchain Based Reinforcement Learning, Nguyen Binh Duong Ta, Umang Chaudhary, Hong-Linh Truong

Research Collection School Of Computing and Information Systems

We propose and implement a decentralized, intelligent air traffic flow management (ATFM) solution to improve the efficiency of air transportation in the ASEAN region as a whole. Our system, named BlockAgent, leverages the inherent synergy between multi-agent reinforcement learning (RL) for air traffic flow optimization; and the rising blockchain technology for a secure, transparent and decentralized coordination platform. As a result, BlockAgent does not require a centralized authority for effective ATFM operations. We have implemented several novel distributed coordination approaches for RL in BlockAgent. Empirical experiments with real air traffic data concerning regional airports have demonstrated the feasibility and effectiveness …

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Splitsecond: Flexible Privilege Separation Of Android Apps, Jehyun Lee, Akshaya Venkateswara Venkateswara Raja, Debin Gao Jul 2019

Splitsecond: Flexible Privilege Separation Of Android Apps, Jehyun Lee, Akshaya Venkateswara Venkateswara Raja, Debin Gao

Research Collection School Of Computing and Information Systems

Android applications have been attractive targets to attackers due to the large number of users and the sensitive information they possess. After the success of the first step of an attack exploiting a software vulnerability, the consequential damage is primarily determined by the criticality and the amount of Android permissions that a victim application has. As a countermeasure, process separation techniques that isolate potentially vulnerable components — usually native libraries — from the critical data and permissions, have been proposed. However, existing techniques offer little flexibility in the separation, e.g., with all native code being placed into one process without …

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Semantic Patches For Java Program Transformation (Artifact), Hong Jin Kang, Thung Ferdian, Julia Lawall, Gilles Muller, Lingxiao Jiang, David Lo Jul 2019

Semantic Patches For Java Program Transformation (Artifact), Hong Jin Kang, Thung Ferdian, Julia Lawall, Gilles Muller, Lingxiao Jiang, David Lo

Research Collection School Of Computing and Information Systems

The program transformation tool Coccinelle is designed for making changes that is required in many locations within a software project. It has been shown to be useful for C code and has been been adopted for use in the Linux kernel by many developers. Over 6000 commits mentioning the use of Coccinelle have been made in the Linux kernel. Our artifact, Coccinelle4J, is an extension to Coccinelle in order for it to apply program transformations to Java source code. This artifact accompanies our experience report “Semantic Patches for Java Program Transformation”, in which we show a case study of applying …

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Eugene: Towards Deep Intelligence As A Service, Shuochao Yao, Yifan Hao, Yiran Zhao, Ailing Piao, Huajie Shao, Dongxin Liu, Shengzhong Liu, Shaohan Hu, Dulanga Weerakoon, Kasthuri Jayarajah, Archan Misra, Tarek Abdelzaher Jul 2019

Eugene: Towards Deep Intelligence As A Service, Shuochao Yao, Yifan Hao, Yiran Zhao, Ailing Piao, Huajie Shao, Dongxin Liu, Shengzhong Liu, Shaohan Hu, Dulanga Weerakoon, Kasthuri Jayarajah, Archan Misra, Tarek Abdelzaher

Research Collection School Of Computing and Information Systems

The paper discusses an emerging suite of machine intelligence services that are of increasing importance in the highly instrumented world of the Internet of Things (IoT). The suite, called Eugene, would offer a form of intelligent behavior (based on deep neural networks) to otherwise simple embedded devices; the clients of the service. These devices would benefit from service resources to learn from data and to perform intelligent inference, classification, prediction, and estimation tasks that they are too limited to carry out on their own. The paper discusses the taxonomy of such services and the state of implementation, as well as …

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Modeling Intra-Relation In Math Word Problems With Different Functional Multi-Head Attentions, Jierui Li, Lei Wang, Jipeng Zhang, Yan Wang, Bing Tian Dai, Dongxiang Zhang Jul 2019

Modeling Intra-Relation In Math Word Problems With Different Functional Multi-Head Attentions, Jierui Li, Lei Wang, Jipeng Zhang, Yan Wang, Bing Tian Dai, Dongxiang Zhang

Research Collection School Of Computing and Information Systems

Several deep learning models have been proposed for solving math word problems (MWPs) automatically. Although these models have the ability to capture features without manual efforts, their approaches to capturing features are not specifically designed for MWPs. To utilize the merits of deep learning models with simultaneous consideration of MWPs’ specific features, we propose a group attention mechanism to extract global features, quantity-related features, quantity-pair features and question-related features in MWPs respectively. The experimental results show that the proposed approach performs significantly better than previous state-of-the-art methods, and boost performance from 66.9% to 69.5% on Math23K with training-test split, from …

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Resource Constrained Deep Reinforcement Learning, Abhinav Bhatia, Pradeep Varakantham, Akshat Kumar Jul 2019

Resource Constrained Deep Reinforcement Learning, Abhinav Bhatia, Pradeep Varakantham, Akshat Kumar

Research Collection School Of Computing and Information Systems

In urban environments, resources have to be constantly matched to the “right” locations where customer demand is present. For instance, ambulances have to be matched to base stations regularly so as to reduce response time for emergency incidents in ERS (Emergency Response Systems); vehicles (cars, bikes among others) have to be matched to docking stations to reduce lost demand in shared mobility systems. Such problems are challenging owing to the demand uncertainty, combinatorial action spaces and constraints on allocation of resources (e.g., total resources, minimum and maximum number of resources at locations and regions). Existing systems typically employ myopic and …

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Compositional Coding For Collaborative Filtering, Chenghao Liu, Tao Lu, Xin Wang, Zhiyong Cheng, Jianling Sun, Steven C. H. Hoi Jul 2019

Compositional Coding For Collaborative Filtering, Chenghao Liu, Tao Lu, Xin Wang, Zhiyong Cheng, Jianling Sun, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Efficiency is crucial to the online recommender systems, especially for the ones which needs to deal with tens of millions of users and items. Because representing users and items as binary vectors for Collaborative Filtering (CF) can achieve fast user-item affinity computation in the Hamming space, in recent years, we have witnessed an emerging research effort in exploiting binary hashing techniques for CF methods. However, CF with binary codes naturally suffers from low accuracy due to limited representation capability in each bit, which impedes it from modeling complex structure of the data. In this work, we attempt to improve the …

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Entropy Based Independent Learning In Anonymous Multi-Agent Settings, Tanvi Verma, Pradeep Varakantham, Hoong Chuin Lau Jul 2019

Entropy Based Independent Learning In Anonymous Multi-Agent Settings, Tanvi Verma, Pradeep Varakantham, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Efficient sequential matching of supply and demand is a problem of interest in many online to offline services. For instance, Uber, Lyft, Grab for matching taxis to customers; Ubereats, Deliveroo, FoodPanda etc for matching restaurants to customers. In these online to offline service problems, individuals who are responsible for supply (e.g., taxi drivers, delivery bikes or delivery van drivers) earn more by being at the ”right” place at the ”right” time. We are interested in developing approaches that learn to guide individuals to be in the ”right” place at the ”right” time (to maximize revenue) in the presence of other …

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An Intelligent Platform With Automatic Assessment And Engagement Features For Active Online Discussions, Michelle L. F. Cheong, Yun-Chen Chen, Bing Tian Dai Jul 2019

An Intelligent Platform With Automatic Assessment And Engagement Features For Active Online Discussions, Michelle L. F. Cheong, Yun-Chen Chen, Bing Tian Dai

Research Collection School Of Computing and Information Systems

In a universitycontext, discussion forums are mostly available in Learning and ManagementSystems (LMS) but are often ineffective in encouraging participation due topoorly designed user interface and the lack of motivating factors toparticipate. Our integrated platform with the Telegram mobile app and aweb-based forum, is capable of automatic thoughtfulness assessment of questionsand answers posted, using text mining and Natural Language Processing (NLP)methodologies. We trained and applied the Random Forest algorithm to provideinstant thoughtfulness score prediction for the new posts contributed by thestudents, and prompted the students to improve on their posts, thereby invokingdeeper thinking resulting in better quality contributions. In addition, …

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Zac: A Zone Path Construction Approach For Effective Real-Time Ridesharing, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet Jul 2019

Zac: A Zone Path Construction Approach For Effective Real-Time Ridesharing, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the right requests to travel in available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. The most relevant existing work has focussed on generating as many relevant feasible (with respect to available delay for customers) combinations of requests (referred to as trips) as possible in real-time. …

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Correlated Learning For Aggregation Systems, Tanvi Verma, Pradeep Varakantham Jul 2019

Correlated Learning For Aggregation Systems, Tanvi Verma, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Aggregation systems (e.g., Uber, Lyft, FoodPanda, Deliveroo) have been increasingly used to improve efficiency in numerous environments, including in transportation, logistics, food and grocery delivery. In these systems, a centralized entity (e.g., Uber) aggregates supply and assigns them to demand so as to optimize a central metric such as profit, number of requests, delay etc. Due to optimizing a metric of importance to the centralized entity, the interests of individuals (e.g., drivers, delivery boys) can be sacrificed. Therefore, in this paper, we focus on the problem of serving individual interests, i.e., learning revenue maximizing policies for individuals in the presence …

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Pruneable Sharding-Based Blockchain Protocol, Xiaoqin Feng, Jianfeng Ma, Yinbin Miao, Qian Meng, Ximeng Liu, Qi Jiang, Hui Li Jul 2019

Pruneable Sharding-Based Blockchain Protocol, Xiaoqin Feng, Jianfeng Ma, Yinbin Miao, Qian Meng, Ximeng Liu, Qi Jiang, Hui Li

Research Collection School Of Computing and Information Systems

As a distributed ledger technology, the block-chain has gained much attention from both the industrical and academical fields, but most of the existing blockchain protocols still have the cubical dilatation problem. Although the latest Rollerchain has mitigated this issue by changing the blockheader's contents, the low efficiency, severe capacity expansion and non-scalability problems still hinder the adoption of Rollerchain in practice. To this end, we present the pruneable sharding-based blockchain protocol by utilizing the sharding technique and PBFT(Practical Byzantine Fault Tolerance) algorithm in the improved Rollerchain, which has high efficiency, slow cubical dilatation, small capacity expansion and high scalability. Moreover, …

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Redpc: A Residual Error-Based Density Peak Clustering Algorithm, Milan Parmar, Di Wang, Xiaofeng Zhang, Ah-Hwee Tan, Chunyan Miao, You Zhou Jul 2019

Redpc: A Residual Error-Based Density Peak Clustering Algorithm, Milan Parmar, Di Wang, Xiaofeng Zhang, Ah-Hwee Tan, Chunyan Miao, You Zhou

Research Collection School Of Computing and Information Systems

The density peak clustering (DPC) algorithm was designed to identify arbitrary-shaped clusters by finding density peaks in the underlying dataset. Due to its aptitudes of relatively low computational complexity and a small number of control parameters in use, DPC soon became widely adopted. However, because DPC takes the entire data space into consideration during the computation of local density, which is then used to generate a decision graph for the identification of cluster centroids, DPC may face difficulty in differentiating overlapping clusters and in dealing with low-density data points. In this paper, we propose a residual error-based density peak clustering …

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The Wiener Attack On Rsa Revisited: A Quest For The Exact Bound, Willy Susilo, Joseph Tonien, Guomin Yang Jul 2019

The Wiener Attack On Rsa Revisited: A Quest For The Exact Bound, Willy Susilo, Joseph Tonien, Guomin Yang

Research Collection School Of Computing and Information Systems

Since Wiener pointed out that the RSA can be broken if the private exponent d is relatively small compared to the modulus N (using the continued fraction technique), it has been a general belief that the Wiener attack works for. On the contrary, in this work, we give an example where the Wiener attack fails with, thus, showing that the bound is not accurate as it has been thought of. By using the classical Legendre Theorem on continued fractions, in 1999 Boneh provided the first rigorous proof which showed that the Wiener attack works for. However, the question remains whether …

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Location Based Encryption, Tran Viet Xuan Phuong, Willy Susilo, Guomin Yang, Jun Yan, Dongxi Liu Jul 2019

Location Based Encryption, Tran Viet Xuan Phuong, Willy Susilo, Guomin Yang, Jun Yan, Dongxi Liu

Research Collection School Of Computing and Information Systems

We first propose a 2D Location Based Encryption (LBE) scheme, where the setting includes a geography center system and the 2D triangle area including the set of locations. A user joining in the system is provided with a pre-arranged key, which belongs to her/his location. If the user’s location is belonging to this area, he/she can decrypt the message. Our proposed scheme achieves a constant ciphertext size in encryption algorithm and decryption cost. Beyond the 2D-LBE scheme, we explore the 3D-LBE scheme; whereby the location is set up in the 3D dimensions. This proposed scheme is an extension of 2D-LBE …

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Multi-Channel Graph Neural Network For Entity Alignment, Yixin Cao, Zhiyuan Liu, Chengjiang Li, Zhiyuan Liu, Juanzi Li, Tat-Seng Chua Jul 2019

Multi-Channel Graph Neural Network For Entity Alignment, Yixin Cao, Zhiyuan Liu, Chengjiang Li, Zhiyuan Liu, Juanzi Li, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments. In this paper, we propose a novel Multi-channel Graph Neural Network model (MuGNN) to learn alignment-oriented knowledge graph (KG) embeddings by robustly encoding two KGs via multiple channels. Each channel encodes KGs via different relation weighting schemes with respect to self-attention towards KG completion and cross-KG attention for pruning exclusive entities respectively, which are further combined via pooling techniques. Moreover, we also infer and transfer rule knowledge for completing two KGs consistently. MuGNN is expected to reconcile the structural differences of two KGs, and thus make …

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Personalized Fashion Recommendation With Visual Explanations Based On Multimodal Attention Network: Towards Visually Explainable Recommendation, Xu Chen, Hanxiong Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Zheng Qin, Hongyuan Zha Jul 2019

Personalized Fashion Recommendation With Visual Explanations Based On Multimodal Attention Network: Towards Visually Explainable Recommendation, Xu Chen, Hanxiong Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Zheng Qin, Hongyuan Zha

Research Collection School Of Computing and Information Systems

Fashion recommendation has attracted increasing attention from both industry and academic communities. This paper proposes a novel neural architecture for fashion recommendation based on both image region-level features and user review information. Our basic intuition is that: for a fashion image, not all the regions are equally important for the users, i.e., people usually care about a few parts of the fashion image. To model such human sense, we learn an attention model over many pre-segmented image regions, based on which we can understand where a user is really interested in on the image, and correspondingly, represent the image in …

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Towards Understanding Android System Vulnerabilities: Techniques And Insights, Daoyuan Wu, Debin Gao, Eric K. T. Cheng, Yichen Cao, Jintao Jiang, Robert H. Deng Jul 2019

Towards Understanding Android System Vulnerabilities: Techniques And Insights, Daoyuan Wu, Debin Gao, Eric K. T. Cheng, Yichen Cao, Jintao Jiang, Robert H. Deng

Research Collection School Of Computing and Information Systems

As a common platform for pervasive devices, Android has been targeted by numerous attacks that exploit vulnerabilities in its apps and the operating system. Compared to app vulnerabilities, systemlevel vulnerabilities in Android, however, were much less explored in the literature. In this paper, we perform the first systematic study of Android system vulnerabilities by comprehensively analyzing all 2,179 vulnerabilities on the Android Security Bulletin program over about three years since its initiation in August 2015. To this end, we propose an automatic analysis framework, upon a hierarchical database structure, to crawl, parse, clean, and analyze vulnerability reports and their publicly …

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Securing Messaging Services Through Efficient Signcryption With Designated Equality Test, Yujue Wang, Hwee Hwa Pang, Robert H. Deng, Yong Ding, Qianhong Wu, Bo Qin Jul 2019

Securing Messaging Services Through Efficient Signcryption With Designated Equality Test, Yujue Wang, Hwee Hwa Pang, Robert H. Deng, Yong Ding, Qianhong Wu, Bo Qin

Research Collection School Of Computing and Information Systems

To address security and privacy issues in messaging services, we present a public key signcryption scheme with designated equality test on ciphertexts (PKS-DET) in this paper. The scheme enables a sender to simultaneously encrypt and sign (signcrypt) messages, and to designate a tester to perform equality test on ciphertexts, i.e., to determine whether two ciphertexts signcrypt the same underlying plaintext message. We introduce the PKS-DET framework, present a concrete construction and formally prove its security against three types of adversaries, representing two security requirements on message confidentiality against outsiders and the designated tester, respectively, and a requirement on message unforgeability …

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