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Articles 7921 - 7950 of 9003
Full-Text Articles in Computer Sciences
Efficient Optimistic Fair Exchange Secure In The Multi-User Setting And Chosen-Key Model Without Random Oracles, Qiong Huang, Guomin Yang, Duncan S. Wong, Willy Susilo
Efficient Optimistic Fair Exchange Secure In The Multi-User Setting And Chosen-Key Model Without Random Oracles, Qiong Huang, Guomin Yang, Duncan S. Wong, Willy Susilo
Research Collection School Of Computing and Information Systems
Optimistic fair exchange is a kind of protocols to solve the problem of fair exchange between two parties. Almost all the previous work on this topic are provably secure only in the random oracle model. In PKC 2007, Dodis et al. considered optimistic fair exchange in a multi-user setting, and showed that the security of an optimistic fair exchange in a single-user setting may no longer be secure in a multi-user setting. Besides, they also proposed one and reviewed several previous construction paradigms and showed that they are secure in the multi-user setting. However, their proofs are either in the …
K-Sketch: A 'Kinetic' Sketch Pad For Novice Animators, Richard C. Davis, Brien Colwell, James A. Landay
K-Sketch: A 'Kinetic' Sketch Pad For Novice Animators, Richard C. Davis, Brien Colwell, James A. Landay
Research Collection School Of Computing and Information Systems
Because most animation tools are complex and timeconsuming to learn and use, most animations today are created by experts. To help novices create a wide range of animations quickly, we have developed a general-purpos informal, 2D animation sketching system called K-Sketch. Field studies investigating the needs of animatorsnd would-be animators helped us collect a library of usage scenarios for our tool. A novel optimization technique enabled us to design an interface that is simultaneously fast, simple, and powerful. The result is a pen-based system that relies on users’ intuitive sense of space and time while still supporting a wide range …
Validating Multi-Column Schema Matchings By Type, Bing Tian Dai, Nick Koudas, Divesh Srivastava, Anthony K.H. Tung, Suresh Venkatasubramanian
Validating Multi-Column Schema Matchings By Type, Bing Tian Dai, Nick Koudas, Divesh Srivastava, Anthony K.H. Tung, Suresh Venkatasubramanian
Research Collection School Of Computing and Information Systems
Validation of multi-column schema matchings is essential for successful database integration. This task is especially difficult when the databases to be integrated contain little overlapping data, as is often the case in practice (e.g., customer bases of different companies). Based on the intuition that values present in different columns related by a schema matching will have similar "semantic type", and that this can be captured using distributions over values ("statistical types"), we develop a method for validating 1-1 and compositional schema matchings. Our technique is based on three key technical ideas. First, we propose a generic measure for comparing two …
E-Government Implementation: A Macro Analysis Of Singapore's E-Government Initiatives, Calvin M.L. Chan, Yi Meng Lau, Shan L. Pan
E-Government Implementation: A Macro Analysis Of Singapore's E-Government Initiatives, Calvin M.L. Chan, Yi Meng Lau, Shan L. Pan
Research Collection School Of Computing and Information Systems
This paper offers a macro perspective of the various activities involved in the implementation of e-government through an interpretive analysis of the various e-government-related initiatives undertaken by the Singapore Government. The analysis lead to the identification of four main components in the implementation of e-government, namely (i) information content, (ii) ICT infrastructure, (iii) e-government infostructure, and (iv) e-government promotion. These four components were then conceptually integrated into the e-Government Implementation Framework. This paper suggests that this framework can either be used as a descriptive tool to organize and coordinate various e-government initiatives, or be used as a prescriptive structure to …
On-Line Discovery Of Hot Motion Paths, Dimitris Sacharidis, Kostas Patroumpas, Manolis Terrovitis, Verena Kantere, Michalis Potamias, Kyriakos Mouratidis, Timos Sellis
On-Line Discovery Of Hot Motion Paths, Dimitris Sacharidis, Kostas Patroumpas, Manolis Terrovitis, Verena Kantere, Michalis Potamias, Kyriakos Mouratidis, Timos Sellis
Research Collection School Of Computing and Information Systems
We consider an environment of numerous moving objects, equipped with location-sensing devices and capable of communicating with a central coordinator. In this setting, we investigate the problem of maintaining hot motion paths, i.e., routes frequently followed by multiple objects over the recent past. Motion paths approximate portions of objects' movement within a tolerance margin that depends on the uncertainty inherent in positional measurements. Discovery of hot motion paths is important to applications requiring classification/profiling based on monitored movement patterns, such as targeted advertising, resource allocation, etc. To achieve this goal, we delegate part of the path extraction process to objects, …
Harmoni: Context-Aware Filtering Of Sensor Data For Continuous Remote Health Monitoring, Iqbal Mohomed, Archan Misra, Maria Ebling, William Jerome
Harmoni: Context-Aware Filtering Of Sensor Data For Continuous Remote Health Monitoring, Iqbal Mohomed, Archan Misra, Maria Ebling, William Jerome
Research Collection School Of Computing and Information Systems
A promising architecture for remote healthcare monitoring involves the use of a pervasive device (such as a cellular phone), which aggregates data from multiple body-worn medical sensors and transmits the data to the backend. Unfortunately, the volume of data generated by increasingly sophisticated continuouslyactive sensors can overwhelm the resources on the mobile device. We propose imbuing the mobile device with the intelligence to perform context-aware filtering of sensor data streams in order to reduce transmissions in cases where the observed data corresponds to the norm expected by the system in a given context. To investigate the efficacy of this technique, …
Processing Transitive Nearest-Neighbor Queries In Multi-Channel Access Environments, Xiao Zhang, Wang-Chien Lee, Prasnjit Mitra, Baihua Zheng
Processing Transitive Nearest-Neighbor Queries In Multi-Channel Access Environments, Xiao Zhang, Wang-Chien Lee, Prasnjit Mitra, Baihua Zheng
Research Collection School Of Computing and Information Systems
Wireless broadcast is an efficient way for information dissemination due to its good scalability [10]. Existing works typically assume mobile devices, such as cell phones and PDAs, can access only one channel at a time. In this paper, we consider a scenario of near future where a mobile device has the ability to process queries using information simultaneously received from multiple channels. We focus on the query processing of the transitive nearest neighbor (TNN) search [19]. Two TNN algorithms developed for a single broadcast channel environment are adapted to our new broadcast enviroment. Based on the obtained insights, we propose …
Integrating Temporal Difference Methods And Self‐Organizing Neural Networks For Reinforcement Learning With Delayed Evaluative Feedback, Ah-Hwee Tan, Ning Lu, Dan Xiao
Integrating Temporal Difference Methods And Self‐Organizing Neural Networks For Reinforcement Learning With Delayed Evaluative Feedback, Ah-Hwee Tan, Ning Lu, Dan Xiao
Research Collection School Of Computing and Information Systems
This paper presents a neural architecture for learning category nodes encoding mappings across multimodal patterns involving sensory inputs, actions, and rewards. By integrating adaptive resonance theory (ART) and temporal difference (TD) methods, the proposed neural model, called TD fusion architecture for learning, cognition, and navigation (TD-FALCON), enables an autonomous agent to adapt and function in a dynamic environment with immediate as well as delayed evaluative feedback (reinforcement) signals. TD-FALCON learns the value functions of the state-action space estimated through on-policy and off-policy TD learning methods, specifically state-action-reward-state-action (SARSA) and Q-learning. The learned value functions are then used to determine the …
Multimodal News Story Clustering With Pairwise Visual Near-Duplicate Constraint, Xiao Wu, Chong-Wah Ngo, Alexander G. Hauptmann
Multimodal News Story Clustering With Pairwise Visual Near-Duplicate Constraint, Xiao Wu, Chong-Wah Ngo, Alexander G. Hauptmann
Research Collection School Of Computing and Information Systems
Story clustering is a critical step for news retrieval, topic mining, and summarization. Nonetheless, the task remains highly challenging owing to the fact that news topics exhibit clusters of varying densities, shapes, and sizes. Traditional algorithms are found to be ineffective in mining these types of clusters. This paper offers a new perspective by exploring the pairwise visual cues deriving from near-duplicate keyframes (NDK) for constraint-based clustering. We propose a constraint-driven co-clustering algorithm (CCC), which utilizes the near-duplicate constraints built on top of text, to mine topic-related stories and the outliers. With CCC, the duality between stories and their underlying …
On Ranking Controversies In Wikipedia: Models And Evaluation, Ba-Quy Vuong, Ee Peng Lim, Aixin Sun, Minh-Tam Le, Hady Wirawan Lauw, Kuiyu Chang
On Ranking Controversies In Wikipedia: Models And Evaluation, Ba-Quy Vuong, Ee Peng Lim, Aixin Sun, Minh-Tam Le, Hady Wirawan Lauw, Kuiyu Chang
Research Collection School Of Computing and Information Systems
Wikipedia 1 is a very large and successful Web 2.0 example. As the number of Wikipedia articles and contributors grows at a very fast pace, there are also increasing disputes occurring among the contributors. Disputes often happen in articles with controversial content. They also occur frequently among contributors who are "aggressive" or controversial in their personalities. In this paper, we aim to identify controversial articles in Wikipedia. We propose three models, namely the Basic model and two Controversy Rank (CR) models. These models draw clues from collaboration and edit history instead of interpreting the actual articles or edited content. While …
Concept Detection: Convergence To Local Features And Opportunities Beyond, Shih-Fu Chang, Junfeng He, Yu-Gang Jiang, Elie El Khoury, Chong-Wah Ngo, Akira Yanagawa, Eric Zavesky
Concept Detection: Convergence To Local Features And Opportunities Beyond, Shih-Fu Chang, Junfeng He, Yu-Gang Jiang, Elie El Khoury, Chong-Wah Ngo, Akira Yanagawa, Eric Zavesky
Research Collection School Of Computing and Information Systems
No abstract provided.
Multi-Echelon Repairable Item Inventory System With Limited Repair Capacity Under Nonstationary Demands, Hoong Chuin Lau, Huawei Song
Multi-Echelon Repairable Item Inventory System With Limited Repair Capacity Under Nonstationary Demands, Hoong Chuin Lau, Huawei Song
Research Collection School Of Computing and Information Systems
Classical multi-echelon repairable item inventory models are based either on steady-state analysis or infinite repair capacity, which may not work well in situations when the demand is nonstationary, or repair capacity is limited. In this paper, we propose an analytical model for evaluating system performance that works well under limited repair capacity and nonstationary demands. Following the METRIC methodology, we then develop an optimisation algorithm to solve the corrective maintenance problem in military logistics. Experimental results show that our approach yields good solutions efficiently. This work has also resulted in a software that has been field-tested by a military organisation.
Probabilistic Sales Forecasting For Small And Medium-Size Business Operations, Randall E. Duran
Probabilistic Sales Forecasting For Small And Medium-Size Business Operations, Randall E. Duran
Research Collection School Of Computing and Information Systems
One of the most important aspects of operating a business is the forecasting of sales and allocation of resources to fulfill sales. Sales assessments are usually based on mental models that are not well defined, may be biased, and are difficult to refine and improve over time. Defining sales forecasting models for small- and medium-size business operations is especially difficult when the number of sales events is small but the revenue per sales event is large. This chapter reviews the challenges of sales forecasting in this environment and describes how incomplete and potentially suspect information can be used to produce …
Face Annotation Using Transductive Kernel Fisher Discriminant, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu
Face Annotation Using Transductive Kernel Fisher Discriminant, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Face annotation in images and videos enjoys many potential applications in multimedia information retrieval. Face annotation usually requires many training data labeled by hand in order to build effective classifiers. This is particularly challenging when annotating faces on large-scale collections of media data, in which huge labeling efforts would be very expensive. As a result, traditional supervised face annotation methods often suffer from insufficient training data. To attack this challenge, in this paper, we propose a novel Transductive Kernel Fisher Discriminant (TKFD) scheme for face annotation, which outperforms traditional supervised annotation methods with few training data. The main idea of …
Private Query On Encrypted Data In Multi-User Setting, Feng Bao, Robert H. Deng, Xuhua Ding, Yanjiang Yang
Private Query On Encrypted Data In Multi-User Setting, Feng Bao, Robert H. Deng, Xuhua Ding, Yanjiang Yang
Research Collection School Of Computing and Information Systems
Searchable encryption schemes allow users to perform keyword based searches on an encrypted database. Almost all existing such schemes only consider the scenario where a single user acts as both the data owner and the querier. However, most databases in practice do not just serve one user; instead, they support search and write operations by multiple users. In this paper, we systematically study searchable encryption in a practical multi-user setting. Our results include a set of security notions for multi-user searchable encryption as well as a construction which is provably secure under the newly introduced security notions.
Columbia University/Vireo-Cityu/Irit Trecvid2008 High-Level Feature Extraction And Interactive Video Search, Shih-Fu Chang, Junfeng He, Yu-Gang Jiang, Elie El Khoury, Chong-Wah Ngo, Akira Yanagawa, Eric Zavesky
Columbia University/Vireo-Cityu/Irit Trecvid2008 High-Level Feature Extraction And Interactive Video Search, Shih-Fu Chang, Junfeng He, Yu-Gang Jiang, Elie El Khoury, Chong-Wah Ngo, Akira Yanagawa, Eric Zavesky
Research Collection School Of Computing and Information Systems
In this report, we present overview and comparative analysis of our HLF detection system, which achieves the top performance among all type-A submissions in 2008. We also describe preliminary evaluation of our video search system, CuZero, in the interactive search task.
Collective Outsourcing To Market (Com): A Market-Based Framework For Information Supply Chain Outsourcing, Fang Fang, Zhiling Guo, Andrew B. Whinston
Collective Outsourcing To Market (Com): A Market-Based Framework For Information Supply Chain Outsourcing, Fang Fang, Zhiling Guo, Andrew B. Whinston
Research Collection School Of Computing and Information Systems
This paper discusses the importance of and a solution to separating the information flow from the physical product flow in a supply chain. Motivated by the inefficient demand forecast caused by information asymmetry and lack of an incentive among supply chain partners to share valuable information, we propose a radically new framework called collective outsourcing to market (COM) to address many information supply chain design challenges. To validate the COM framework, we consider a supply chain with one manufacturer and multiple downstream retailers. Retailers privately acquire demand forecast information that they do not have incentive to share horizontally with other …
Enhancing Recursive Supervised Learning Using Clustering And Combinatorial Optimization (Rsl-Cc), Kiruthika Ramanathan, Sheng Uei Guan
Enhancing Recursive Supervised Learning Using Clustering And Combinatorial Optimization (Rsl-Cc), Kiruthika Ramanathan, Sheng Uei Guan
Research Collection School Of Computing and Information Systems
The use of a team of weak learners to learn a dataset has been shown better than the use of one single strong learner. In fact, the idea is so successful that boosting, an algorithm combining several weak learners for supervised learning, has been considered to be one of the best off-the-shelf classifiers. However, some problems still remain, including determining the optimal number of weak learners and the overfitting of data. In an earlier work, we developed the RPHP algorithm which solves both these problems by using a combination of genetic algorithm, weak learner and pattern distributor. In this paper, …
A Growth-Theoretic Empirical Analysis Of Simultaneity In Cross-National E-Commerce Development, Shu-Chun Ho, Robert J. Kauffman, Ting-Peng Liang
A Growth-Theoretic Empirical Analysis Of Simultaneity In Cross-National E-Commerce Development, Shu-Chun Ho, Robert J. Kauffman, Ting-Peng Liang
Research Collection School Of Computing and Information Systems
The emergence of information and communication technologies infrastructure has transformed the global economy. The development of information technology infrastructure is limited to some developed countries though. This research explores the role of information technology infrastructure in B2C e-commerce growth at the country-level from the perspective of growth theory in economics. We propose a hybrid exogenous and endogenous growth model to explain e-commerce growth. We estimate a panel data model that incorporates the direct effects of e-commerce infrastructure and other key explanatory variables. We further specify a simultaneous effects model that permits the analysis of reverse causality in the association between …
The Oil Drilling Model And Iterative Deepening Genetic Annealing Algorithm For The Traveling Salesman Problem, Hoong Chuin Lau, Fei Xiao
The Oil Drilling Model And Iterative Deepening Genetic Annealing Algorithm For The Traveling Salesman Problem, Hoong Chuin Lau, Fei Xiao
Research Collection School Of Computing and Information Systems
In this work, we liken the solving of combinatorial optimization problems under a prescribed computational budget as hunting for oil in an unexplored ground. Using this generic model, we instantiate an iterative deepening genetic annealing (IDGA) algorithm, which is a variant of memetic algorithms. Computational results on the traveling salesman problem show that IDGA is more effective than standard genetic algorithms or simulated annealing algorithms or a straightforward hybrid of them. Our model is readily applicable to solve other combinatorial optimization problems.
Factors Affecting The Information Quality Of Personal Web Portfolios, P. Katerattanakul, Keng Siau
Factors Affecting The Information Quality Of Personal Web Portfolios, P. Katerattanakul, Keng Siau
Research Collection School Of Computing and Information Systems
Personal Web portfolios have become a popular information source and an effective method for individuals to present themselves to others in cyberspace. Thus, the quality of personal Web portfolios is critical and affects the perception that others have of the individuals. But how do we measure quality of personal Web portfolios? What are the important factors affecting quality of personal Web portfolios? This study presents the development of an instrument measuring factors affecting information quality of personal Web portfolios. The proposed instrument, based on the Information Quality framework, was refined and validated to assess its construct validity, convergent validity, and …
Document Selection For Extracting Entity And Relationship Instances Of Terrorist Events, Zhen Sun, Ee Peng Lim, Kuiyu Chang, Maggy Anastasia Suryanto, Rohan Kumar Gunaratna
Document Selection For Extracting Entity And Relationship Instances Of Terrorist Events, Zhen Sun, Ee Peng Lim, Kuiyu Chang, Maggy Anastasia Suryanto, Rohan Kumar Gunaratna
Research Collection School Of Computing and Information Systems
In this chapter, we study the problem of selecting documents so as to extract terrorist event information from a collection of documents. We represent an event by its entity and relation instances. Very often, these entity and relation instances have to be extracted from multiple documents. We therefore define an information extraction (IE) task as selecting documents and extracting from which entity and relation instances relevant to a user-specified event (aka domain specific event entity and relation extraction). We adopt domain specific IE patterns to extract potentially relevant entity and relation instances from documents, and develop a number of document …
Preventing Location-Based Identity Inference In Anonymous Spatial Queries, Panos Kalnis, Gabriel Ghinita, Kyriakos Mouratidis, Dimitris Papadias
Preventing Location-Based Identity Inference In Anonymous Spatial Queries, Panos Kalnis, Gabriel Ghinita, Kyriakos Mouratidis, Dimitris Papadias
Research Collection School Of Computing and Information Systems
The increasing trend of embedding positioning capabilities (for example, GPS) in mobile devices facilitates the widespread use of location-based services. For such applications to succeed, privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safeguard communication channels, and on pseudonyms to protect user identities. Nevertheless, the query contents may disclose the physical location of the user. In this paper, we present a framework for preventing location-based identity inference of users who issue spatial queries to location-based services. We propose transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without …
Competition In Modular Clusters, Carliss Y. Baldwin, C. Jason Woodard
Competition In Modular Clusters, Carliss Y. Baldwin, C. Jason Woodard
Research Collection School Of Computing and Information Systems
The last twenty years have witnessed the rise of disaggregated “clusters,” “networks,” or “ecosystems” of firms. In these clusters the activities of R&D, product design, production, distribution, and system integration may be split up among hundreds or even thousands of firms. Different firms will design and produce the different components of a complex artifact (like the processor, peripherals, and software of a computer system), and different firms will specialize in different stages of a complex production process. This paper considers the pricing behavior and profitability of these so-called modular clusters. In particular, we investigate a possibility hinted at in prior …
Multi-Order Neurons For Evolutionary Higher Order Clustering And Growth, Kiruthika Ramanathan, Sheng Uei Guan
Multi-Order Neurons For Evolutionary Higher Order Clustering And Growth, Kiruthika Ramanathan, Sheng Uei Guan
Research Collection School Of Computing and Information Systems
This letter proposes to use multiorder neurons for clustering irregularly shaped data arrangements. Multiorder neurons are an evolutionary extension of the use of higher-order neurons in clustering. Higher-order neurons parametrically model complex neuron shapes by replacing the classic synaptic weight by higher-order tensors. The multiorder neuron goes one step further and eliminates two problems associated with higher-order neurons. First, it uses evolutionary algorithms to select the best neuron order for a given problem. Second, it obtains more information about the underlying data distribution by identifying the correct order for a given cluster of patterns. Empirically we observed that when the …
Study Of The Minimum Spanning Hyper-Tree Routing Algorithm In Wireless Sensor Networks, Ting Yang, Yugeng Sun, Zhaoxia Wang, Juwei Zhang, Yingqiang Ding
Study Of The Minimum Spanning Hyper-Tree Routing Algorithm In Wireless Sensor Networks, Ting Yang, Yugeng Sun, Zhaoxia Wang, Juwei Zhang, Yingqiang Ding
Research Collection School Of Computing and Information Systems
Designing energy-efficient routing protocols to effectively increase the networks' lifetime and provide the robust network service is one of the important problems in the research of wireless sensor networks. Using the hyper-graph theory, the paper represents large-scale wireless sensor networks into a hyper-graph model, which can effectively decrease the control messages in routing process. Based on this mathematic model, the paper presents the minimum spanning hyper-tree routing algorithm in synchronous wireless sensor networks (MSHT-SN), which builds a minimum energy consumption tree for data collection from multi-nodes to Sink node. The validity of the algorithm is proved by the theatrical analysis. …
A Unified Interdisciplinary Theory Of Open Source Culture And Entertainment, Jerald Hughes, Karl Reiner Lang, Eric K. Clemons, Robert J. Kauffman
A Unified Interdisciplinary Theory Of Open Source Culture And Entertainment, Jerald Hughes, Karl Reiner Lang, Eric K. Clemons, Robert J. Kauffman
Research Collection School Of Computing and Information Systems
Digital technologies are profoundly transforming the production and consumption of culture and entertainment products. The emerging digital re-mix culture is an open source approach where content products in the arts and entertainment industries are increasingly rearranged, manipulated, and extended in the process of creating new works. This article offers a unified description of the tools and techniques that led to the development of the open source culture and that enable the processes which promote re-use of previously recorded materials. It then lays out the incentives and forces that either promote or inhibit the development, distribution, and consumption of modified cultural …
Self-Organizing Neural Architectures And Cooperative Learning In A Multiagent Environment, Dan Xiao, Ah-Hwee Tan
Self-Organizing Neural Architectures And Cooperative Learning In A Multiagent Environment, Dan Xiao, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Temporal-Difference–Fusion Architecture for Learning, Cognition, and Navigation (TD-FALCON) is a generalization of adaptive resonance theory (a class of self-organizing neural networks) that incorporates TD methods for real-time reinforcement learning. In this paper, we investigate how a team of TD-FALCON networks may cooperate to learn and function in a dynamic multiagent environment based on minefield navigation and a predator/prey pursuit tasks. Experiments on the navigation task demonstrate that TD-FALCON agent teams are able to adapt and function well in a multiagent environment without an explicit mechanism of collaboration. In comparison, traditional Q-learning agents using gradient-descent-based feedforward neural networks, trained with the …
Understanding Highly Competent Information System Users, Brenda Eschenbrenner, Fiona Fui-Hoon Nah
Understanding Highly Competent Information System Users, Brenda Eschenbrenner, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
Individuals differ in their abilities to use information systems (IS) effectively, with some achieving exceptional performance in IS use. Using the Repertory Grid Technique, this research identifies attributes of highly competent IS users that distinguish them from less competent users. Using the Grounded Theory approach, we identified categories and sub-categories of these attributes and used them to develop a conceptual framework to explain IS User Competency. The findings indicate that highly competent users differ from less competent users in their Personality Traits and Disposition Factors, General Cognitive Abilities, Social Skills and Tendencies, Experiential Learning Factors, Domain Knowledge of and Skills …
Tuning Tabu Search Strategies Via Visual Diagnosis, Steven Halim, Hoong Chuin Lau
Tuning Tabu Search Strategies Via Visual Diagnosis, Steven Halim, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
While designing working metaheuristics can be straightforward, tuning them to solve the underlying combinatorial optimization problem well can be tricky. Several tuning methods have been proposed but they do not address the new aspect of our proposed classification of the metaheuristic tuning problem: tuning search strategies. We propose a tuning methodology based on Visual Diagnosis and a generic tool called Visualizer for Metaheuristics Development Framework(V-MDF) to address specifically the problem of tuning search (particularly Tabu Search) strategies. Under V-MDF, we propose the use of a Distance Radar visualizer where the human and computer can collaborate to diagnose the occurrence of …