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Articles 4381 - 4410 of 9025
Full-Text Articles in Computer Sciences
Jobsense: A Data-Driven Career Knowledge Exploration Framework And System, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim, Philips Kokoh Prasetyo
Jobsense: A Data-Driven Career Knowledge Exploration Framework And System, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim, Philips Kokoh Prasetyo
Research Collection School Of Computing and Information Systems
Today’s job market sees rapid changes due to technology and business model disruptions. To fully tap on one’s potential in career development, one has to acquire job and skill knowledge through working on different jobs. Another approach is to seek consultation with career coaches who are trained to offer career advice in various industry sectors. The above two approaches, nevertheless, suffer from several shortcomings. The on-the-job career development approach is highly inefficient for today’s fast changing job market. The latter career coach assisted approach could help to speed up knowledge acquisition but it relies on expertise of career coaches but …
Linky: Visualizing User Identity Linkage Results For Multiple Online Social Networks (Demo), Roy Ka-Wei Lee, Ming Shan Hee, Philips Kokoh Prasetyo, Ee-Peng Lim
Linky: Visualizing User Identity Linkage Results For Multiple Online Social Networks (Demo), Roy Ka-Wei Lee, Ming Shan Hee, Philips Kokoh Prasetyo, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
User identity linkage across online social networks is an emerging research topic that has attracted attention in recent years. Many user identity linkage methods have been proposed so far and most of them utilize user profile, content and network information to determine if two social media accounts belong to the same person. In most cases, user identity linkage methods are evaluated by performing some prediction tasks with the results presented using some overall accuracy measures. However, the methods are rarely compared at the individual user level where a predicted matched (or linked) pair of user identities from different online social …
Improving Multi-Label Emotion Classification Via Sentiment Classification With Dual Attention Transfer Network, Jianfei Yu, Luis Marujo, Jing Jiang, Pradeep Karuturi, William Brendel
Improving Multi-Label Emotion Classification Via Sentiment Classification With Dual Attention Transfer Network, Jianfei Yu, Luis Marujo, Jing Jiang, Pradeep Karuturi, William Brendel
Research Collection School Of Computing and Information Systems
In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Extensive experimental results demonstrate that our transfer learning approach can outperform several strong baselines and achieve the state-of-the-art performance on two benchmark datasets.
Ten Years Of Hunting For Similar Code For Fun And Profit (Keynote), Stephane Glondu, Lingxiao Jiang, Zhendong Su
Ten Years Of Hunting For Similar Code For Fun And Profit (Keynote), Stephane Glondu, Lingxiao Jiang, Zhendong Su
Research Collection School Of Computing and Information Systems
In 2007, the Deckard paper was published at ICSE. Since its publication, it has led to much follow-up research and applications. The paper made two core contributions: a novel vector embedding of structured code for fast similarity detection, and an application of the embedding for clone detection, resulting in the Deckard tool. The vector embedding is simple and easy to adapt. Similar code detection is also fundamental for a range of classical and emerging problems in software engineering, security, and computer science education (e.g., code reuse, refactoring, porting, translation, synthesis, program repair, malware detection, and feedback generation). Both have buttressed …
Vt-Revolution: Interactive Programming Tutorials Made Possible, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Shanping Li
Vt-Revolution: Interactive Programming Tutorials Made Possible, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
Programming video tutorials showcase programming tasks and associated workflows. Although video tutorials are easy to create, it isoften difficult to explore the captured workflows and interact withthe programs in the videos. In this work, we propose a tool named VTRevolution – an interactive programming video tutorial authoring system. VTRevolution has two components: 1) a tutorial authoring system leverages operating system level instrumentation to log workflow history while tutorial authors are creating programming video tutorials; 2) a tutorial watching system enhances the learning experience of video tutorials by providing operation history and timeline-based browsing interactions. Our tutorial authoring system does not …
Infar: Insight Extraction From App Reviews, Cuiyun Gao, Jichuan Zeng, David Lo, Chin-Yew Lin, Michael R. Lyu, Irwin King
Infar: Insight Extraction From App Reviews, Cuiyun Gao, Jichuan Zeng, David Lo, Chin-Yew Lin, Michael R. Lyu, Irwin King
Research Collection School Of Computing and Information Systems
App reviews play an essential role for users to convey their feedback about using the app. The critical information contained in app reviews can assist app developers for maintaining and updating mobile apps. However, the noisy nature and large-quantity of daily generated app reviews make it difficult to understand essential information carried in app reviews. Several prior studies have proposed methods that can automatically classify or cluster user reviews into a few app topics (e.g., security). These methods usually act on a static collection of user reviews. However, due to the dynamic nature of user feedback (i.e., reviews keep coming …
Dsm: A Specification Mining Tool Using Recurrent Neural Network Based Language Model, Tien-Duy B. Le, Lingfeng Bao, David Lo
Dsm: A Specification Mining Tool Using Recurrent Neural Network Based Language Model, Tien-Duy B. Le, Lingfeng Bao, David Lo
Research Collection School Of Computing and Information Systems
Formal specifications are important but often unavailable. Furthermore, writing these specifications is time-consuming and requires skills from developers. In this work, we present Deep Specification Miner (DSM), an automated tool that applies deep learning to mine finite-state automaton (FSA) based specifications. DSM accepts as input a set of execution traces to train a Recurrent Neural Network Language Model (RNNLM). From the input traces, DSM creates a Prefix Tree Acceptor (PTA) and leverages the inferred RNNLM to extract many features. These features are then forwarded to clustering algorithms for merging similar automata states in the PTA for assembling a number of …
Using Finite-State Models For Log Differencing, Hen Amar, Lingfeng Bao, Nimrod Busany, David Lo, Shahar Maoz
Using Finite-State Models For Log Differencing, Hen Amar, Lingfeng Bao, Nimrod Busany, David Lo, Shahar Maoz
Research Collection School Of Computing and Information Systems
Much work has been published on extracting various kinds of models from logs that document the execution of running systems. In many cases, however, for example in the context of evolution, testing, or malware analysis, engineers are interested not only in a single log but in a set of several logs, each of which originated from a different set of runs of the system at hand. Then, the difference between the logs is the main target of interest. In this work we investigate the use of finite-state models for log differencing. Rather than comparing the logs directly, we generate concise …
Improving Reusability Of Software Libraries Through Usage Pattern Mining, Mohamed Aymen Saied, Ali Ouni, Houari A. Sahraoui, Raula Gaikovina Kula, Katsuro Inoue, David Lo
Improving Reusability Of Software Libraries Through Usage Pattern Mining, Mohamed Aymen Saied, Ali Ouni, Houari A. Sahraoui, Raula Gaikovina Kula, Katsuro Inoue, David Lo
Research Collection School Of Computing and Information Systems
Modern software systems are increasingly dependent on third-party libraries. It is widely recognized that using mature and well-tested third-party libraries can improve developers’ productivity, reduce time-to-market, and produce more reliable software. Today’s open-source repositories provide a wide range of libraries that can be freely downloaded and used. However, as software libraries are documented separately but intended to be used together, developers are unlikely to fully take advantage of these reuse opportunities. In this paper, we present a novel approach to automatically identify third-party library usage patterns, i.e., collections of libraries that are commonly used together by developers. Our approach employs …
Recommending Who To Follow In The Software Engineering Twitter Space, Abhabhisheksh Sharma, Yuan Tian, Agus Sulistya, Dinusha Wijedasa, David Lo
Recommending Who To Follow In The Software Engineering Twitter Space, Abhabhisheksh Sharma, Yuan Tian, Agus Sulistya, Dinusha Wijedasa, David Lo
Research Collection School Of Computing and Information Systems
With the advent of social media, developers are increasingly using it in their software development activities. Twitter is one of the popular social mediums used by developers. A recent study by Singer et al. found that software developers use Twitter to “keep up with the fast-paced development landscape.” Unfortunately, due to the general-purpose nature of Twitter, it’s challenging for developers to use Twitter for their development activities. Our survey with 36 developers who use Twitter in their development activities highlights that developers are interested in following specialized software gurus who share relevant technical tweets.To help developers perform this task, in …
Improving Knowledge Tracing Model By Integrating Problem Difficulty, Sein Minn, Feida Zhu, Michel C. Desmarais
Improving Knowledge Tracing Model By Integrating Problem Difficulty, Sein Minn, Feida Zhu, Michel C. Desmarais
Research Collection School Of Computing and Information Systems
Intelligent Tutoring Systems (ITS) are designed for providing personalized instructions to students with the needs of their skills. Assessment of student knowledge acquisition dynamically is nontrivial during her learning process with ITS. Knowledge tracing, a popular student modeling technique for student knowledge assessment in adaptive tutoring, which is used for tracing student's knowledge state and detecting student's knowledge acquisition by using decomposed individual skill or problems with a single skill per problem. Unfortunately, recent KT models fail to deal with practices of complex skill composition and variety of concepts included in a problem simultaneously. Our goal is to investigate a …
Is There Space For Violence?: A Data-Driven Approach To The Exploration Of Spatial-Temporal Dimensions Of Conflict, Tin Seong Kam, Vincent Zhi
Is There Space For Violence?: A Data-Driven Approach To The Exploration Of Spatial-Temporal Dimensions Of Conflict, Tin Seong Kam, Vincent Zhi
Research Collection School Of Computing and Information Systems
With recent increases in incidences of political violence globally, the world has now become more uncertain and less predictable. Of particular concern is the case of violence against civilians, who are often caught in the crossfire between armed state or non-state actors. Classical methods of studying political violence and international relations need to be updated. Adopting the use of data analytic tools and techniques of studying big data would enable academics and policy makers to make sense of a rapidly changing world.
Centroid-Amenities: An Interactive Visual Analytical Tool For Exploring And Analyzing Amenities In Singapore, Xue Qian Jazreel Siew, Sean Jia Ming Koh
Centroid-Amenities: An Interactive Visual Analytical Tool For Exploring And Analyzing Amenities In Singapore, Xue Qian Jazreel Siew, Sean Jia Ming Koh
Research Collection School Of Computing and Information Systems
Planning for civic amenities in a fast-changing urban setting such as Singapore is never an easy task. And as urban planners look toward more data-driven approaches toward urban planning, so grows the demand for more flexible geospatial analytics tools to facilitate a more iterative and granular approach toward urban planning. Such specific tools however, are not always readily available as plugins for traditional desktop GIS software, as numerous customizations must be made to model specific temporal planning scenarios for quick analysis, which could prove both costly and time-consuming. Hence, to address this need, open-source tools such as R Shiny could …
Designing A Tangible Interface For Manager Awareness In Wilderness Search And Rescue, Brennan Jones, Anthony Tang, Carman Neustaedter, Alissa N. Antle, Elgin-Skye Mclaren
Designing A Tangible Interface For Manager Awareness In Wilderness Search And Rescue, Brennan Jones, Anthony Tang, Carman Neustaedter, Alissa N. Antle, Elgin-Skye Mclaren
Research Collection School Of Computing and Information Systems
We present a tangible interface for supporting wilderness search-and-rescue (SAR) managers in maintaining awareness of a large SAR incident, where there are numerous field teams searching for a lost person in a wilderness area. This interface consists of physical and digital representations of the search area and elements of the search activity (e.g., the locations of search teams, weather information, and clues from the field). It is intended to allow SAR managers to inspect information about the response and search area from different perspectives and aid them in planning by allowing them to physically manipulate the representations and explore the …
Double Learning Or Double Blinding: An Investigation Of Vendor Private Information Acquisition And Consumer Learning Via Online Reviews, Nan Hu, Kevin E. Dow, Alain Yee Loong Chong, Ling Liu
Double Learning Or Double Blinding: An Investigation Of Vendor Private Information Acquisition And Consumer Learning Via Online Reviews, Nan Hu, Kevin E. Dow, Alain Yee Loong Chong, Ling Liu
Research Collection School Of Computing and Information Systems
In this paper, building upon information acquisition theory and using portfolio methods and system equations, we made an empirical investigation into how online vendors and consumers are learning from each other, and how online reviews, prices, and sales interact among each other. First, this study shows that vendors acquire information from both private and public channels to learn the quality of their products to make price adjustment. Second, for the more popular products and newly released products, vendors are more motivated to acquire private information that is more precise than the average precision to adjust their price. Third, we document …
Differentially Private Location Protection With Continuous Time Stamps For Vanets, Zhili Chen, Xianyue Bao, Zuobin Ying, Ximeng Liu, Hong Zhong
Differentially Private Location Protection With Continuous Time Stamps For Vanets, Zhili Chen, Xianyue Bao, Zuobin Ying, Ximeng Liu, Hong Zhong
Research Collection School Of Computing and Information Systems
Vehicular Ad hoc Networks (VANETs) have higher requirements of continuous Location-Based Services (LBSs). However, the untrusted server could reveal the users’ location privacy in the meantime. Syntactic-based privacy models have been widely used in most of the existing location privacy protection schemes. Whereas, they are suffering from background knowledge attacks, neither do they take the continuous time stamps into account. Therefore we propose a new differential privacy definition in the context of location protection for the VANETs, and we designed an obfuscation mechanism so that fine-grained locations and trajectories will not exposed when vehicles request location-based services on continuous time …
Identifying Elderly With Poor Sleep Quality Using Unobtrusive In-Home Sensors For Early Intervention, Xiaoping Ma, W K P Neranjana Nadee Rodrigo Goonawardene, Hwee-Pink Tan
Identifying Elderly With Poor Sleep Quality Using Unobtrusive In-Home Sensors For Early Intervention, Xiaoping Ma, W K P Neranjana Nadee Rodrigo Goonawardene, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
Along with the upward trend in population ageing is the increasing proportion of the elderly population living alone in the community. This group is especially vulnerable as the onset of various physical, social and mental health issues may be more likely and may go undetected. However, smart homes enabled with elderly monitoring and care systems (EMCS) can now be used to alert caregivers of anomalies in the daily living patterns of the elderly. In this study, we focus on the sleep quality as the key living pattern, as it has been shown that poor sleep quality can lead to health …
Pestel Analysis Of Hacktivism Campaign Motivations, Mikko Samuli Niemelae
Pestel Analysis Of Hacktivism Campaign Motivations, Mikko Samuli Niemelae
Research Collection Lee Kong Chian School Of Business
A political, economic, socio-cultural, technological, environment and legal (PESTEL) analysis is a framework or tool used to analyse and monitor the macro-environmental factors that have an impact on an organisation. The results identify threats and weaknesses which are used in a strengths, weaknesses, opportunities and threats (SWOT) analysis. In this paper the PESTEL framework was utilized to categorize hacktivism motivations for attack campaigns against certain companies, governments or industries. Our study is based on empirical evidence: of thirty-three hacktivism attack campaigns in manifesto level. Then, the targets of these campaigns were analysed and studied accordingly. As a result, we claim …
Aligning Technical Debt Prioritization With Business Objectives: A Multiple-Case Study, Rodrigo Rebouças De Almeida, Uirá Kulesza, Christoph Treude, D’Angellys Cavalcanti Feitosa, Aliandro Higino Guedes Lima
Aligning Technical Debt Prioritization With Business Objectives: A Multiple-Case Study, Rodrigo Rebouças De Almeida, Uirá Kulesza, Christoph Treude, D’Angellys Cavalcanti Feitosa, Aliandro Higino Guedes Lima
Research Collection School Of Computing and Information Systems
Technical debt (TD) is a metaphor to describe the trade-off between short-term workarounds and long-term goals in software development. Despite being widely used to explain technical issues in business terms, industry and academia still lack a proper way to manage technical debt while explicitly considering business priorities. In this paper, we report on a multiple-case study of how two big software development companies handle technical debt items, and we show how taking the business perspective into account can improve the decision making for the prioritization of technical debt. We also propose a first step toward an approach that uses business …
Artefact: An R Implementation Of The Autospearman Function, Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude
Artefact: An R Implementation Of The Autospearman Function, Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude
Research Collection School Of Computing and Information Systems
This artefact is the implementation of AutoSpearman, an automated metric selection approach based on correlation analyses. The goal of AutoSpearman is to automatically mitigate correlated metrics prior to constructing analytical models. This artefact is implemented as an R package and is available in the GitHub repository. We provide descriptions and R code snippets for the installation of AutoSpearman and usage examples.
Autospearman: Automatically Mitigating Correlated Software Metrics For Interpreting Defect Models, Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude
Autospearman: Automatically Mitigating Correlated Software Metrics For Interpreting Defect Models, Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude
Research Collection School Of Computing and Information Systems
The interpretation of defect models heavily relies on software metrics that are used to construct them. However, such software metrics are often correlated in defect models. Prior work often uses feature selection techniques to remove correlated metrics in order to improve the performance of defect models. Yet, the interpretation of defect models may be misleading if feature selection techniques produce subsets of inconsistent and correlated metrics. In this paper, we investigate the consistency and correlation of the subsets of metrics that are produced by nine commonly-used feature selection techniques. Through a case study of 13 publicly-available defect datasets, we find …
Gesture Recognition With Transparent Solar Cells, Dong Ma, Guohao Lan, Mahbub Hassan, Wen Hu, B. Mushfika Upama, Ashraf Uddin, Youseef, Moustafa
Gesture Recognition With Transparent Solar Cells, Dong Ma, Guohao Lan, Mahbub Hassan, Wen Hu, B. Mushfika Upama, Ashraf Uddin, Youseef, Moustafa
Research Collection School Of Computing and Information Systems
Transparent solar cell is an emerging solar energy harvesting technology that allows us to see through these cells. This revolutionary discovery is creating unique opportunities to turn any mobile device screen into solar energy harvester. In this paper, we consider the possibility of using such energy harvesting screens as a sensor to detect hand gestures. As different gestures impact the incident light on the screen in a different way, they are expected to create unique energy generation patterns for the transparent solar cell. Our goal is to recognize gestures by detecting these solar energy patterns. A key uncertainty we face …
Privacy-Preserving Communication And Power Injection Over Vehicle Networks And 5g Smart Grid Slice, Yinghui Zhang, Jin Li, Dong Zheng, Ping Li, Yangguang Tian
Privacy-Preserving Communication And Power Injection Over Vehicle Networks And 5g Smart Grid Slice, Yinghui Zhang, Jin Li, Dong Zheng, Ping Li, Yangguang Tian
Research Collection School Of Computing and Information Systems
As an important combination of autonomous vehicle networks (AVNs) and smart grid, the vehicle-to-grid (V2G) network can facilitate the adoption of renewable resources. Based on V2G networks, parked electric vehicles (EVs) can charge during off-peak hours and inject excess power to the grid during peak hours for earnings. However, each EV's power injection bids in V2G are sensitive and vehicle-to-vehicle (V2V) communication may be eavesdropped, which has become an obstacle to the wide deployments of AVNs. Aiming to efficiently tackle these security and privacy issues in AVNs, we propose an efficient privacy-preserving communication and power injection (ePPCP) scheme without pairings, …
Imaginary People Representing Real Numbers: Generating Personas From Online Social Media Data, Jisun An, Haewoon Kwak, Soongyo Jung, Joni Salminen, M. Admad, Bernard J. Jansen
Imaginary People Representing Real Numbers: Generating Personas From Online Social Media Data, Jisun An, Haewoon Kwak, Soongyo Jung, Joni Salminen, M. Admad, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
We develop a methodology to automate creating imaginary people, referred to as personas, by processing complex behavioral and demographic data of social media audiences. From a popular social media account containing more than 30 million interactions by viewers from 198 countries engaging with more than 4,200 online videos produced by a global media corporation, we demonstrate that our methodology has several novel accomplishments, including: (a) identifying distinct user behavioral segments based on the user content consumption patterns; (b) identifying impactful demographics groupings; and (c) creating rich persona descriptions by automatically adding pertinent attributes, such as names, photos, and personal characteristics. …
Comparing Elm With Svm In The Field Of Sentiment Classification Of Social Media Text Data, Zhihuan Chen, Zhaoxia Wang, Zhiping Lin, Ting Yang
Comparing Elm With Svm In The Field Of Sentiment Classification Of Social Media Text Data, Zhihuan Chen, Zhaoxia Wang, Zhiping Lin, Ting Yang
Research Collection School Of Computing and Information Systems
Machine learning has been used in various fields with thousands of applications. Extreme learning machine (ELM), which is the most recently developed machine learning algorithm, has become increasingly popular for its good generalization ability. However, it has been relatively less applied to the domain of social media. Support Vector Machine (SVM), another popular learning-based algorithm, has been applied for sentiment classification of social media text data and has obtained good results. This paper investigates and compares the capabilities of these two learning-based methods in the field of sentiment classification of social media. The results indicate that SVM can obtain good …
Cross-Modal Recipe Retrieval With Stacked Attention Model, Jing-Jing Chen, Lei Pang, Chong-Wah Ngo
Cross-Modal Recipe Retrieval With Stacked Attention Model, Jing-Jing Chen, Lei Pang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Taking a picture of delicious food and sharing it in social media has been a popular trend. The ability to recommend recipes along will benefit users who want to cook a particular dish, and the feature is yet to be available. The challenge of recipe retrieval, nevertheless, comes from two aspects. First, the current technology in food recognition can only scale up to few hundreds of categories, which are yet to be practical for recognizing tens of thousands of food categories. Second, even one food category can have variants of recipes that differ in ingredient composition. Finding the best-match recipe …
March Of The Silent Bots, Paul Robert Griffin
March Of The Silent Bots, Paul Robert Griffin
MITB Thought Leadership Series
Self-intelligent software robots, or ‘bots’ are everywhere. These small pieces of code run automated tasks when you order a taxi, search for a restaurant or check the weather. Quietly beavering away, it is unknown how many bots exist, but undoubtedly this number is set to surge over time. Already, bots comprise roughly half of all internet traffic.
Disruptive Technology: Can The Banking Industry Harness Disruption For Competitive Edge?, Edgar Low
Disruptive Technology: Can The Banking Industry Harness Disruption For Competitive Edge?, Edgar Low
MITB Thought Leadership Series
Disruptive innovation was identified as a phenomenon more than two decades ago by prominent Harvard scholar Clayton Christensen. So you may wonder why established industries are only now waking up to the prospect of digital transformation - the banking industry in particular.
Geometry-Aware Similarity Learning On Spd Manifolds For Visual Recognition, Zhiwu Huang, R. Wang, X. Li, W. Liu, S. Shan, Gool L. Van, X Chen
Geometry-Aware Similarity Learning On Spd Manifolds For Visual Recognition, Zhiwu Huang, R. Wang, X. Li, W. Liu, S. Shan, Gool L. Van, X Chen
Research Collection School Of Computing and Information Systems
Symmetric positive definite (SPD) matrices have been employed for data representation in many visual recognition tasks. The success is mainly attributed to learning discriminative SPD matrices encoding the Riemannian geometry of the underlying SPD manifolds. In this paper, we propose a geometry-aware SPD similarity learning (SPDSL) framework to learn discriminative SPD features by directly pursuing a manifold-manifold transformation matrix of full column rank. Specifically, by exploiting the Riemannian geometry of the manifolds of fixed-rank positive semidefinite (PSD) matrices, we present a new solution to reduce optimization over the space of column full-rank transformation matrices to optimization on the PSD manifold, …
Deep Understanding Of Cooking Procedure For Cross-Modal Recipe Retrieval, Jingjing Chen, Chong-Wah Ngo, Fu-Li Feng, Tat-Seng Chua
Deep Understanding Of Cooking Procedure For Cross-Modal Recipe Retrieval, Jingjing Chen, Chong-Wah Ngo, Fu-Li Feng, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Finding a right recipe that describes the cooking procedure for a dish from just one picture is inherently a difficult problem. Food preparation undergoes a complex process involving raw ingredients, utensils, cutting and cooking operations. This process gives clues to the multimedia presentation of a dish (e.g., taste, colour, shape). However, the description of the process is implicit, implying only the cause of dish presentation rather than the visual effect that can be vividly observed on a picture. Therefore, different from other cross-modal retrieval problems in the literature, recipe search requires the understanding of textually described procedure to predict its …