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Articles 2851 - 2880 of 4266
Full-Text Articles in Entire DC Network
Imagespirit: Verbal Guided Image Parsing, Ming-Ming Cheng, Shuai Zheng, Wen-Yan Lin, Vibhav Vineet, Paul Sturgess, Nigel Crook, Niloy J. Mitra, Philip Torr
Imagespirit: Verbal Guided Image Parsing, Ming-Ming Cheng, Shuai Zheng, Wen-Yan Lin, Vibhav Vineet, Paul Sturgess, Nigel Crook, Niloy J. Mitra, Philip Torr
Research Collection School Of Computing and Information Systems
Humans describe images in terms of nouns and adjectives while algorithms operate on images represented as sets of pixels. Bridging this gap between how humans would like to access images versus their typical representation is the goal of image parsing, which involves assigning object and attribute labels to pixels. In this article we propose treating nouns as object labels and adjectives as visual attribute labels. This allows us to formulate the image parsing problem as one of jointly estimating per-pixel object and attribute labels from a set of training images. We propose an efficient (interactive time) solution. Using the extracted …
Dynamic Clustering Of Contextual Multi-Armed Bandits, Trong T. Nguyen, Hady W. Lauw
Dynamic Clustering Of Contextual Multi-Armed Bandits, Trong T. Nguyen, Hady W. Lauw
Research Collection School Of Computing and Information Systems
With the prevalence of the Web and social media, users increasingly express their preferences online. In learning these preferences, recommender systems need to balance the trade-off between exploitation, by providing users with more of the "same", and exploration, by providing users with something "new" so as to expand the systems' knowledge. Multi-armed bandit (MAB) is a framework to balance this trade-off. Most of the previous work in MAB either models a single bandit for the whole population, or one bandit for each user. We propose an algorithm to divide the population of users into multiple clusters, and to customize the …
Generative Modeling Of Entity Comparisons In Text, Maksim Tkachenko, Hady W. Lauw
Generative Modeling Of Entity Comparisons In Text, Maksim Tkachenko, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Users frequently rely on online reviews for decision making. In addition to allowing users to evaluate the quality of individual products, reviews also support comparison shopping. One key user activity is to compare two (or more) products based on a specific aspect. However, making a comparison across two different reviews, written by different authors, is not always equitable due to the different standards and preferences of individual authors. Therefore, we focus instead on comparative sentences, whereby two products are compared directly by a review author within a single sentence. We study the problem of comparative relation mining. Given a set …
Stopwatch: A Cloud Architecture For Timing Channel Mitigation, Peng Li, Debin Gao, Michael K Reiter
Stopwatch: A Cloud Architecture For Timing Channel Mitigation, Peng Li, Debin Gao, Michael K Reiter
Research Collection School Of Computing and Information Systems
This article presents StopWatch, a system that defends against timing-based side-channel attacks that arise from coresidency of victims and attackers in infrastructure-as-a-service clouds. StopWatch triplicates each cloud-resident guest virtual machine (VM) and places replicas so that the three replicas of a guest VM are coresident with nonoverlapping sets of (replicas of) other VMs. StopWatch uses the timing of I/O events at a VM’s replicas collectively to determine the timings observed by each one or by an external observer, so that observable timing behaviors are similarly likely in the absence of any other individual, coresident VMs. We detail the design and …
Online Passive Aggressive Active Learning And Its Applications, Jing Lu, Peilin Zhao, Steven C. H. Hoi
Online Passive Aggressive Active Learning And Its Applications, Jing Lu, Peilin Zhao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
We investigate online active learning techniques for classification tasks in data stream mining applications. Unlike traditional learning approaches (either batch or online learning) that often require to request the class label of each incoming instance, online active learning queries only a subset of informative incoming instances to update the classification model, which aims to maximize classification performance using minimal human labeling effort during the entire online stream data mining task. In this paper, we present a new family of algorithms for online active learning called Passive-Aggressive Active (PAA) learning algorithms by adapting the popular Passive-Aggressive algorithms in an online active …
Vector Abstraction And Concretization For Scalable Detection Of Refactorings, Narcisa Andreea Milea, Lingxiao Jiang, Siau-Cheng Khoo
Vector Abstraction And Concretization For Scalable Detection Of Refactorings, Narcisa Andreea Milea, Lingxiao Jiang, Siau-Cheng Khoo
Research Collection School Of Computing and Information Systems
Automated techniques have been proposed to either identify refactoring opportunities (i.e., code fragments that can be but have not yet been restructured in a program), or reconstruct historical refactorings (i.e., code restructuring operations that have happened between different versions of a program). In this paper, we propose a new technique that can detect both refactoring opportunities and historical refactorings in large code bases. The key of our technique is the design of vector abstraction and concretization operations that can encode code changes induced by certain refactorings as characteristic vectors. Thus, the problem of identifying refactorings can be reduced to the …
Grumon: Fast And Accurate Group Monitoring For Heterogeneous Urban Spaces, Rijurekha Sen, Youngki Lee, Kasthuri Jayarajah, Rajesh Krishna Balan, Archan Misra
Grumon: Fast And Accurate Group Monitoring For Heterogeneous Urban Spaces, Rijurekha Sen, Youngki Lee, Kasthuri Jayarajah, Rajesh Krishna Balan, Archan Misra
Research Collection School Of Computing and Information Systems
Real-time monitoring of groups and their rich contexts will be a key building block for futuristic, group-aware mobile services. In this paper, we propose GruMon, a fast and accurate group monitoring system for dense and complex urban spaces. GruMon meets the performance criteria of precise group detection at low latencies by overcoming two critical challenges of practical urban spaces, namely (a) the high density of crowds, and (b) the imprecise location information available indoors. Using a host of novel features extracted from commodity smartphone sensors, GruMon can detect over 80% of the groups, with 97% precision, using 10 minutes latency …
Compositional Vector Space Models For Improved Bug Localization, Shaowei Wang, David Lo, Julia Lawall
Compositional Vector Space Models For Improved Bug Localization, Shaowei Wang, David Lo, Julia Lawall
Research Collection School Of Computing and Information Systems
Software developers and maintainers often need to locate code units responsible for a particular bug. A number of Information Retrieval (IR) techniques have been proposed to map natural language bug descriptions to the associated code units. The vector space model (VSM) with the standard tf-idf weighting scheme (VSMnatural), has been shown to outperform nine other state-of-the-art IR techniques. However, there are multiple VSM variants with different weighting schemes, and their relative performance differs for different software systems. Based on this observation, we propose to compose various VSM variants, modelling their composition as an optimization problem. We propose a genetic algorithm …
Dynamic Inference Of Change Contracts, Tien-Duy B. Le, Jooyong Yi, David Lo, Ferdian Thung, Abhik Roychoudhury
Dynamic Inference Of Change Contracts, Tien-Duy B. Le, Jooyong Yi, David Lo, Ferdian Thung, Abhik Roychoudhury
Research Collection School Of Computing and Information Systems
Software evolves and thus developers frequently make changes to systems that are logged in version control systems. These changes are often poorly documented -- often commit logs are empty or only contain minimal information. Thus, it is often a challenge to understand why certain changes are made especially if they were introduced many months or even years ago. Understanding these changes is important when pertinent questions are raised during future bug fixing or software evolution efforts. Thus, there is a need for an automated approach that can help developers better document changes with little or minimal effort. To address this …
Entity Linking On Microblogs With Spatial And Temporal Signals, Yuan Fang, Ming-Wei Chang
Entity Linking On Microblogs With Spatial And Temporal Signals, Yuan Fang, Ming-Wei Chang
Research Collection School Of Computing and Information Systems
Microblogs present an excellent opportunity for monitoring and analyzing world happenings. Given that words are often ambiguous, entity linking becomes a crucial step towards understanding microblogs. In this paper, we re-examine the problem of entity linking on microblogs. We first observe that spatiotemporal (i.e., spatial and temporal) signals play a key role, but they are not utilized in existing approaches. Thus, we propose a novel entity linking framework that incorporates spatiotemporal signals through a weakly supervised process. Using entity annotations1 on real-world data, our experiments show that the spatiotemporal model improves F1 by more than 10 points over existing systems. …
Partisan Sharing: Facebook Evidence And Societal Consequences, Jisun An, Daniele Quercia, Jon Crowcroft
Partisan Sharing: Facebook Evidence And Societal Consequences, Jisun An, Daniele Quercia, Jon Crowcroft
Research Collection School Of Computing and Information Systems
The hypothesis of selective exposure assumes that people seek out information that supports their views and eschew information that conflicts with their beliefs, and that has negative consequences on our society. Few researchers have recently found counter evidence of selective exposure in social media: users are exposed to politically diverse articles. No work has looked at what happens after exposure, particularly how individuals react to such exposure, though. Users might well be exposed to diverse articles but share only the partisan ones. To test this, we study partisan sharing on Facebook: the tendency for users to predominantly share like-minded news …
Attribute-Based Signing Right Delegation, Weiwei Liu, Yi Mu, Guomin Yang
Attribute-Based Signing Right Delegation, Weiwei Liu, Yi Mu, Guomin Yang
Research Collection School Of Computing and Information Systems
Attribute-based signature and proxy signature are both very useful in many real-world applications. In this paper, we combine the special features of both signatures and propose an attribute-based proxy signature scheme, where the original signer, who possesses a set of attributes, can delegate his/her signing right to a designated proxy signer. By verifying the signature, a verifier can be convinced that the signature is generated by the proxy signer who has obtained the delegation from a legitimate signer whose attributes satisfy a predicate. However, the verifier cannot tell from the signature who is the original signer. We provide the formal …
Cost-Sensitive Online Classification, Jialei Wang, Peilin Zhao, Steven C. H. Hoi
Cost-Sensitive Online Classification, Jialei Wang, Peilin Zhao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Both cost-sensitive classification and online learning have been extensively studied in data mining and machine learning communities, respectively. However, very limited study addresses an important intersecting problem, that is, “Cost-Sensitive Online Classification". In this paper, we formally study this problem, and propose a new framework for Cost-Sensitive Online Classification by directly optimizing cost-sensitive measures using online gradient descent techniques. Specifically, we propose two novel cost-sensitive online classification algorithms, which are designed to directly optimize two well-known cost-sensitive measures: (i) maximization of weighted sum of sensitivity and specificity, and (ii) minimization of weighted misclassification cost. We analyze the theoretical bounds of …
Distributed Event Detection In Sensor Networks Under Random Spatial Deployment, P. Zhang, G. Peters, I. Nevat, G. Xiao, Hwee-Pink Tan
Distributed Event Detection In Sensor Networks Under Random Spatial Deployment, P. Zhang, G. Peters, I. Nevat, G. Xiao, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
We present a novel event detection algorithm in sensor networks for the case where the sensors are randomly deployed in space. In particular we consider a random sensors deployment according to a Homogeneous Finite Binomial Point Process. We first derive the optimal event detection decision rule. We then develop a novel algorithm to evaluate the intractable marginal likelihood based on the Gram-Charlier series expansion. We evaluate our algorithms through extensive Monte Carlo simulations. Simulation results present the detection and false alarm rates for different system parameters such as number of sensors deployed, deployment region size etc.
We Can Hear You With Wifi!, Guanhua Wang, Yongpan Zou, Zimu Zhou, Kaishun Wu, Lionel M. Ni
We Can Hear You With Wifi!, Guanhua Wang, Yongpan Zou, Zimu Zhou, Kaishun Wu, Lionel M. Ni
Research Collection School Of Computing and Information Systems
Recent literature advances Wi-Fi signals to “see” people’s motions and locations. This paper asks the following question: Can Wi-Fi “hear” our talks? We present WiHear, which enables Wi-Fi signals to “hear” our talks without deploying any devices. To achieve this, WiHear needs to detect and analyze fine-grained radio reflections from mouth movements. WiHear solves this micro-movement detection problem by introducing Mouth Motion Profile that leverages partial multipath effects and wavelet packet transformation. Since Wi-Fi signals do not require line-of-sight, WiHear can “hear” people talks within the radio range. Further, WiHear can simultaneously “hear” multiple people’s talks leveraging MIMO technology. We …
Build Emotion Lexicon From Microblogs By Combining Effects Of Seed Words And Emoticons In A Heterogeneous Graph, Kaisong Song, Shi Feng, Wei Gao, Daling Wang, Ling Chen, Chengqi Zhang
Build Emotion Lexicon From Microblogs By Combining Effects Of Seed Words And Emoticons In A Heterogeneous Graph, Kaisong Song, Shi Feng, Wei Gao, Daling Wang, Ling Chen, Chengqi Zhang
Research Collection School Of Computing and Information Systems
As an indispensable resource for emotion analysis, emotion lexicons have attracted increasing attention in recent years. Most existing methods focus on capturing the single emotional effect of words rather than the emotion distributions which are helpful to model multiple complex emotions in a subjective text. Meanwhile, automatic lexicon building methods are overly dependent on seed words but neglect the effect of emoticons which are natural graphical labels of fine-grained emotion. In this paper, we propose a novel emotion lexicon building framework that leverages both seed words and emoticons simultaneously to capture emotion distributions of candidate words more accurately. Our method …
Verifiable Computation On Outsourced Encrypted Data, Junzuo Lai, Robert H. Deng, Hwee Hwa Pang, Jian Weng
Verifiable Computation On Outsourced Encrypted Data, Junzuo Lai, Robert H. Deng, Hwee Hwa Pang, Jian Weng
Research Collection School Of Computing and Information Systems
On one hand, homomorphic encryption allows a cloud server to perform computation on outsourced encrypted data but provides no verifiability that the computation is correct. On the other hand, homomorphic authenticator, such as homomorphic signature with public verifiability and homomorphic MAC with private verifiability, guarantees authenticity of computation over outsourced data but does not provide data confidentiality. Since cloud servers are usually operated by third-party providers which are almost certain to be outside the trust domain of cloud users, neither homomorphic encryption nor homomorphic authenticator suffices for verifiable computation on outsourced encrypted data in the cloud. In this paper, we …
Graph Matching By Simplified Convex-Concave Relaxation Procedure, Zhiyong Liu, Hong Qiao, Xu Yang, Steven C. H. Hoi
Graph Matching By Simplified Convex-Concave Relaxation Procedure, Zhiyong Liu, Hong Qiao, Xu Yang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
The convex and concave relaxation procedure (CCRP) was recently proposed and exhibited state-of-the-art performance on the graph matching problem. However, CCRP involves explicitly both convex and concave relaxations which typically are difficult to find, and thus greatly limit its practical applications. In this paper we propose a simplified CCRP scheme, which can be proved to realize exactly CCRP, but with a much simpler formulation without needing the concave relaxation in an explicit way, thus significantly simplifying the process of developing CCRP algorithms. The simplified CCRP can be generally applied to any optimizations over the partial permutation matrix, as long as …
Potential Biases In Bug Localization: Do They Matter?, Pavneet Singh Kochhar, Yuan Tian, David Lo
Potential Biases In Bug Localization: Do They Matter?, Pavneet Singh Kochhar, Yuan Tian, David Lo
Research Collection School Of Computing and Information Systems
Issue tracking systems are valuable resources during software maintenance activities and contain information about the issues faced during the development of a project as well as after its release. Many projects receive many reports of bugs and it is challenging for developers to manually debug and fix them. To mitigate this problem, past studies have proposed information retrieval (IR)-based bug localization techniques, which takes as input a textual description of a bug stored in an issue tracking system, and returns a list of potentially buggy source code files. These studies often evaluate their effectiveness on issue reports marked as bugs …
Sharing Political News: The Balancing Act Of Intimacy And Socialization In Selective Exposure, Jisun An, Daniele Quercia, Meeyoung Cha, Krishna Gummadi, Jon Crowcroft
Sharing Political News: The Balancing Act Of Intimacy And Socialization In Selective Exposure, Jisun An, Daniele Quercia, Meeyoung Cha, Krishna Gummadi, Jon Crowcroft
Research Collection School Of Computing and Information Systems
One might think that, compared to traditional media, social media sites allow people to choose more freely what to read and what to share, especially for politically oriented news. However, reading and sharing habits originate from deeply ingrained behaviors that might be hard to change. To test the extent to which this is true, we propose a Political News Sharing (PoNS) model that holistically captures four key aspects of social psychology: gratification, selective exposure, socialization, and trust & intimacy. Using real instances of political news sharing in Twitter, we study the predictive power of these features. As one might expect, …
Saliency Detection With Flash And No-Flash Image Pairs, Shengfeng He, Rynson W.H. Lau
Saliency Detection With Flash And No-Flash Image Pairs, Shengfeng He, Rynson W.H. Lau
Research Collection School Of Computing and Information Systems
In this paper, we propose a new saliency detection method using a pair of flash and no-flash images. Our approach is inspired by two observations. First, only the foreground objects are significantly brightened by the flash as they are relatively nearer to the camera than the background. Second, the brightness variations introduced by the flash provide hints to surface orientation changes. Accordingly, the first observation is explored to form the background prior to eliminate background distraction. The second observation provides a new orientation cue to compute surface orientation contrast. These photometric cues from the two observations are independent of visual …
Crossnavi: Enabling Real-Time Crossroad Navigation For The Blind With Commodity Phones, Longfei Shangguan, Zheng Yang, Zimu Zhou, Xiaolong Zheng, Chenshu Wu, Yunhao Liu
Crossnavi: Enabling Real-Time Crossroad Navigation For The Blind With Commodity Phones, Longfei Shangguan, Zheng Yang, Zimu Zhou, Xiaolong Zheng, Chenshu Wu, Yunhao Liu
Research Collection School Of Computing and Information Systems
Crossroad is among the most dangerous parts outside for the visually impaired people. Numerous studies have exploited navigating systems for the visually impaired community, providing services ranging from block detection, route planning to realtime localization. However, none of them have addressed the safety issue in crossroad and integrated three key factors necessary for a practical crossroad navigation system: detecting the crossroad, locating zebra patterns, and guiding the user within zebra crossing when passing the road. Our CrossNavi application responds to these needs, providing an integrated crossroad navigation service that incorporates all the essential functionalities mentioned above. The overall service is …
Event Analytics, Jin Song Dong, Jun Sun, Yang Liu, Yuan-Fang Li
Event Analytics, Jin Song Dong, Jun Sun, Yang Liu, Yuan-Fang Li
Research Collection School Of Computing and Information Systems
The process analysis toolkit (PAT) integrates the expressiveness of state, event, time, and probability-based languages with the power of model checking. PAT is a self-contained reasoning system for system specification, simulation, and verification. PAT currently supports a wide range of 12 different expressive modeling languages with many application domains and has attracted thousands of registered users from hundreds of organizations. In this invited talk, we will present the PAT system and its vision on “Event Analytics” (EA) which is beyond “Data Analytics”. The EA research is based on applying model checking to event planning, scheduling, prediction, strategy analysis and decision …
Online Probabilistic Learning For Fuzzy Inference System, Richard Jayadi Oentaryo, Meng Joo Er, San Linn, Xiang Li
Online Probabilistic Learning For Fuzzy Inference System, Richard Jayadi Oentaryo, Meng Joo Er, San Linn, Xiang Li
Research Collection School Of Computing and Information Systems
Online learning is a key methodology for expert systems to gracefully cope with dynamic environments. In the context of neuro-fuzzy systems, research efforts have been directed toward developing online learning methods that can update both system structure and parameters on the fly. However, the current online learning approaches often rely on heuristic methods that lack a formal statistical basis and exhibit limited scalability in the face of large data stream. In light of these issues, we develop a new Sequential Probabilistic Learning for Adaptive Fuzzy Inference System (SPLAFIS) that synergizes the Bayesian Adaptive Resonance Theory (BART) and Rule-Wise Decoupled Extended …
Phaser: Enabling Phased Array Signal Processing On Commodity Wifi Access Points, Jon Gjengset, Jie Xiong, Graeme Mcphilips, Kyle Jamieson
Phaser: Enabling Phased Array Signal Processing On Commodity Wifi Access Points, Jon Gjengset, Jie Xiong, Graeme Mcphilips, Kyle Jamieson
Research Collection School Of Computing and Information Systems
Signal processing on antenna arrays has received much recent attention in the mobile and wireless networking research communities, with array signal processing approaches addressing the problems of human movement detection, indoor mobile device localization, and wireless network security. However, there are two important challenges inherent in the design of these systems that must be overcome if they are to be of practical use on commodity hardware. First, phase differences between the radio oscillators behind each antenna can make readings unusable, and so must be corrected in order for most techniques to yield high-fidelity results. Second, while the number of antennas …
Control Flow Obfuscation Using Neural Network To Fight Concolic Testing, Haoyu Ma, Xinjie Ma, Weijie Liu, Zhipeng Huang, Debin Gao, Chunfu Jia
Control Flow Obfuscation Using Neural Network To Fight Concolic Testing, Haoyu Ma, Xinjie Ma, Weijie Liu, Zhipeng Huang, Debin Gao, Chunfu Jia
Research Collection School Of Computing and Information Systems
Concolic testing is widely regarded as the state-of-the-art technique in dynamic discovering and analyzing trigger-based behavior in software programs. It uses symbolic execution and an automatic theorem prover to generate new concrete test cases to maximize code coverage for scenarios like software verification and malware analysis. While malicious developers usually try their best to hide malicious executions, there are also circumstances in which legitimate reasons are presented for a program to conceal trigger-based conditions and the corresponding behavior, which leads to the demand of control flow obfuscation techniques. We propose a novel control flow obfuscation design based on the incomprehensibility …
To What Extent Could We Detect Field Defects? An Extended Empirical Study Of False Negatives In Static Bug Finding Tools, Ferdian Thung, Lucia Lucia, David Lo, Lingxiao Jiang, Foyzur Rahman, Premkumar Devanbu
To What Extent Could We Detect Field Defects? An Extended Empirical Study Of False Negatives In Static Bug Finding Tools, Ferdian Thung, Lucia Lucia, David Lo, Lingxiao Jiang, Foyzur Rahman, Premkumar Devanbu
Research Collection School Of Computing and Information Systems
Software defects can cause much loss. Static bug-finding tools are designed to detect and remove software defects and believed to be effective. However, do such tools in fact help prevent actual defects that occur in the field and reported by users? If these tools had been used, would they have detected these field defects, and generated warnings that would direct programmers to fix them? To answer these questions, we perform an empirical study that investigates the effectiveness of five state-of-the-art static bug-finding tools (FindBugs, JLint, PMD, CheckStyle, and JCSC) on hundreds of reported and fixed defects extracted from three open …
Efficient Hidden Vector Encryption With Constant-Size Ciphertext, Tran Viet Xuan Phuong, Guomin Yang, Willy Susilo
Efficient Hidden Vector Encryption With Constant-Size Ciphertext, Tran Viet Xuan Phuong, Guomin Yang, Willy Susilo
Research Collection School Of Computing and Information Systems
A Hidden Vector Encryption (HVE) scheme is a special type of anonymous identity-based encryption (IBE) scheme where the attribute string associated with the ciphertext or the user secret key can contain wildcards. In this paper, we introduce two constant-size ciphertext-policy hidden vector encryption (CP-HVE) schemes. Our first scheme is constructed on composite order bilinear groups, while the second one is built on prime order bilinear groups. Both schemes are proven secure in a selective security model which captures plaintext (or payload) and attribute hiding. To the best of our knowledge, our schemes are the first HVE constructions that can achieve …
Interestingness-Driven Diffussion Process Summarization In Dynamic Networks, Qiang Qu, Siyuan Liu, Christian Jensen, Feida Zhu, Christos Faloutsos
Interestingness-Driven Diffussion Process Summarization In Dynamic Networks, Qiang Qu, Siyuan Liu, Christian Jensen, Feida Zhu, Christos Faloutsos
Research Collection School Of Computing and Information Systems
The widespread use of social networks enables the rapid diffusion of information, e.g., news, among users in very large communities. It is a substantial challenge to be able to observe and understand such diffusion processes, which may be modeled as networks that are both large and dynamic. A key tool in this regard is data summarization. However, few existing studies aim to summarize graphs/networks for dynamics. Dynamic networks raise new challenges not found in static settings, including time sensitivity and the needs for online interestingness evaluation and summary traceability, which render existing techniques inapplicable. We study the topic of dynamic …
Towards Rigorously Faking Bidirectional Model Transformations, Christopher M. Poskitt, Mike Dodds, Richard F. Paige, Arend Rensink
Towards Rigorously Faking Bidirectional Model Transformations, Christopher M. Poskitt, Mike Dodds, Richard F. Paige, Arend Rensink
Research Collection School Of Computing and Information Systems
Bidirectional model transformations (bx) are mechanisms for automatically restoring consistency between multiple concurrently modified models. They are, however, challenging to implement; many model transformation languages not supporting them at all. In this paper, we propose an approach for automatically obtaining the consistency guarantees of bx without the complexities of a bx language. First, we show how to "fake" true bidirectionality using pairs of unidirectional transformations and inter-model consistency constraints in Epsilon. Then, we propose to automatically verify that these transformations are consistency preserving – thus indistinguishable from true bx – by defining translations to graph rewrite rules and nested conditions, …