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Articles 6421 - 6450 of 8479
Full-Text Articles in Computer Sciences
Measurement-Driven Performance Analysis Of Indoor Femtocellular Networks, Trung-Tuan Luong, Vigneshwaran Subbaraju, Archan Misra, Srinivasan Seshan
Measurement-Driven Performance Analysis Of Indoor Femtocellular Networks, Trung-Tuan Luong, Vigneshwaran Subbaraju, Archan Misra, Srinivasan Seshan
Research Collection School Of Computing and Information Systems
This paper describes initial empirical studies, performed on a 6-node 3G indoor femtocellular testbed, that investigate the impact of pedestrian mobility on network parameters, such as handoff behavior and data throughput. The studies establish that, owing to the small radii of cells, even modest changes in movement speed can have disproportionately large impact on handoff patterns and network throughput. By also revealing a strong temporal dependency effect, the studies motivate the need for algorithms to accurately predict RF signal strength distributions in dynamic indoor environments. We present such an RF prediction algorithm, based on crowd-sourced signal strength readings, and show …
Confidence-Aware Graph Regularization With Heterogeneous Pairwise Features, Yuan Fang, Bo-June Paul Hsu, Kevin Chen-Chuan Chang
Confidence-Aware Graph Regularization With Heterogeneous Pairwise Features, Yuan Fang, Bo-June Paul Hsu, Kevin Chen-Chuan Chang
Research Collection School Of Computing and Information Systems
Conventional classification methods tend to focus on features of individual objects, while missing out on potentially valuable pairwise features that capture the relationships between objects. Although recent developments on graph regularization exploit this aspect, existing works generally assume only a single kind of pairwise feature, which is often insufficient. We observe that multiple, heterogeneous pairwise features can often complement each other and are generally more robust in modeling the relationships between objects. Furthermore, as some objects are easier to classify than others, objects with higher initial classification confidence should be weighed more towards classifying related but more ambiguous objects, an …
Message From General Chair And Program Co-Chairs [Of Icec '12, 14th Annual International Conference On Electronic Commerce, Held In Singapore, 7-8 August 2012], Robert J. Kauffman, Martin Bichler, Hoong Chuin Lau, Christopher Yang, Yinping Yang
Message From General Chair And Program Co-Chairs [Of Icec '12, 14th Annual International Conference On Electronic Commerce, Held In Singapore, 7-8 August 2012], Robert J. Kauffman, Martin Bichler, Hoong Chuin Lau, Christopher Yang, Yinping Yang
Research Collection School Of Computing and Information Systems
Singapore, a major hub in the Asia Pacific region well known for its multi-racial and multicultural society, is proud to host the 14th International Conference on Electronic Commerce. Singapore Management University (SMU), the School of Information Systems (SIS) and the Living Analytics Research Center (LARC) are also delighted to be able to support the delivery of this event.
Collective Churn Prediction In Social Network, Richard J. Oentaryo, Ee-Peng Lim, David Lo, Feida Zhu, Philips K. Prasetyo
Collective Churn Prediction In Social Network, Richard J. Oentaryo, Ee-Peng Lim, David Lo, Feida Zhu, Philips K. Prasetyo
Research Collection School Of Computing and Information Systems
In service-based industries, churn poses a significant threat to the integrity of the user communities and profitability of the service providers. As such, research on churn prediction methods has been actively pursued, involving either intrinsic, user profile factors or extrinsic, social factors. However, existing approaches often address each type of factors separately, thus lacking a comprehensive view of churn behaviors. In this paper, we propose a new churn prediction approach based on collective classification (CC), which accounts for both the intrinsic and extrinsic factors by utilizing the local features of, and dependencies among, individuals during prediction steps. We evaluate our …
Control-Theoretic Utility Maximization In Multihop Wireless Networks Under Mission Dynamics, Sharanya Eswaran, Archan Misra, Thomas La Porta
Control-Theoretic Utility Maximization In Multihop Wireless Networks Under Mission Dynamics, Sharanya Eswaran, Archan Misra, Thomas La Porta
Research Collection School Of Computing and Information Systems
Both bandwidth and energy become important resource constraints when multi-hop wireless networks are used to transport high data rate traffic for a moderately long duration. In such networks, it is important to control the traffic rates to not only conform to the link capacity bounds but also to ensure that the energy of battery-powered forwarding nodes is utilized judiciously to avoid premature exhaustion (i.e., the network lasts as long as the applications require data from the sources) without being unneccesarily conservative (i.e., ensuring that the applications derive the maximum utility possible). Unlike prior work that focuses on the instantaneous distributed …
Shortest Path Computation With No Information Leakage, Kyriakos Mouratidis, Man Lung Yiu
Shortest Path Computation With No Information Leakage, Kyriakos Mouratidis, Man Lung Yiu
Research Collection School Of Computing and Information Systems
Shortest path computation is one of the most common queries in location-based services (LBSs). Although particularly useful, such queries raise serious privacy concerns. Exposing to a (potentially untrusted) LBS the client’s position and her destination may reveal personal information, such as social habits, health condition, shopping preferences, lifestyle choices, etc. The only existing method for privacy-preserving shortest path computation follows the obfuscation paradigm; it prevents the LBS from inferring the source and destination of the query with a probability higher than a threshold. This implies, however, that the LBS still deduces some information (albeit not exact) about the client’s location …
The Case For Cloud-Enabled Mobile Sensing Services, Sougata Sen, Archan Misra, Rajesh Krishna Balan, Lipyeow Lim
The Case For Cloud-Enabled Mobile Sensing Services, Sougata Sen, Archan Misra, Rajesh Krishna Balan, Lipyeow Lim
Research Collection School Of Computing and Information Systems
We make the case for cloud-enabled mobile sensing services that support an emerging application class, one which infers near-real time collective context using sensor data obtained continuously from a large set of consumer mobile devices. We present the high-level architecture and functional requirements for such a mobile sensing service, and argue that such a service can significantly improve the scalability and energy-efficiency of large-scale mobile sensing by coordinating the sensing & processing tasks across multiple devices. We then focus specifically on the problem of energy efficiency and provide early exemplars of how optimizing query execution jointly over multiple phones can …
Uncertain Congestion Games With Assorted Human Agent Populations, Asrar Ahmed, Pradeep Reddy Varakantham, Shih-Fen Cheng
Uncertain Congestion Games With Assorted Human Agent Populations, Asrar Ahmed, Pradeep Reddy Varakantham, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
Congestion games model a wide variety of real-world resource congestion problems, such as selfish network routing, traffic route guidance in congested areas, taxi fleet optimization and crowd movement in busy areas. However, existing research in congestion games assumes: (a) deterministic movement of agents between resources; and (b) perfect rationality (i.e. maximizing their own expected value) of all agents. Such assumptions are not reasonable in dynamic domains where decision support has to be provided to humans. For instance, in optimizing the performance of a taxi fleet serving a city, movement of taxis can be involuntary or nondeterministic (decided by the specific …
Dynamic Stochastic Orienteering Problems For Risk-Aware Applications, Hoong Chuin Lau, William Yeoh, Pradeep Varakantham, Duc Thien Nguyen
Dynamic Stochastic Orienteering Problems For Risk-Aware Applications, Hoong Chuin Lau, William Yeoh, Pradeep Varakantham, Duc Thien Nguyen
Research Collection School Of Computing and Information Systems
Orienteering problems (OPs) are a variant of the well-known prize-collecting traveling salesman problem, where the salesman needs to choose a subset of cities to visit within a given deadline. OPs and their extensions with stochastic travel times (SOPs) have been used to model vehicle routing problems and tourist trip design problems. However, they suffer from two limitations travel times between cities are assumed to be time independent and the route provided is independent of the risk preference (with respect to violating the deadline) of the user. To address these issues, we make the following contributions: We introduce (1) a dynamic …
Toward Large-Scale Agent Guidance In An Urban Taxi Service, Agussurja Lucas, Hoong Chuin Lau
Toward Large-Scale Agent Guidance In An Urban Taxi Service, Agussurja Lucas, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Empty taxi cruising represents a wastage of resources in the context of urban taxi services. In this work, we seek to minimize such wastage. An analysis of a large trace of taxi operations reveals that the services’ inefficiency is caused by drivers’ greedy cruising behavior. We model the existing system as a continuous time Markov chain. To address the problem, we propose that each taxi be equipped with an intelligent agent that will guide the driver when cruising for passengers. Then, drawing from AI literature on multiagent planning, we explore two possible ways to compute such guidance. The first formulation …
Bidder Behaviors In Repeated B2b Procurement Auctions, Jong Han Park, Jae Kyu Lee, Hoong Chuin Lau
Bidder Behaviors In Repeated B2b Procurement Auctions, Jong Han Park, Jae Kyu Lee, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
B2B auctions play a key role in a firm's procurement process. Even though it is known that repetition is a key characteristic of procurement auctions, traditional auctioneers typically have not put in place a suitable mechanism that supports repetitive auctions effectively. In this paper, we empirically investigate what has taken place in repeated procurement auctions based on real world data from a major outsourcing company of MRO (Maintenance, Repair and Operations) items in Korea. From this empirical study, we discovered the followings. First, we discovered that the repeated bidders contribute majority of all bids, and that the number of new …
A Secure And Efficient Discovery Service System In Epcglobal Network, Jie Shi, Yingjiu Li, Robert H. Deng
A Secure And Efficient Discovery Service System In Epcglobal Network, Jie Shi, Yingjiu Li, Robert H. Deng
Research Collection School Of Computing and Information Systems
In recent years, the Internet of Things (IOT) has drawn considerable attention from the industrial and research communities. Due to the vast amount of data generated through IOT devices and users, there is an urgent need for an effective search engine to help us make sense of this massive amount of data. With this motivation, we begin our initial works on developing a secure and efficient search engine (SecDS) based on EPC Discovery Services (EPCDS) for EPCglobal network, an integral part of IOT. SecDS is designed to provide a bridge between different partners of supply chains to share information while …
(Hidden) Social Influences In Switching Mobile Service Platforms, Virpi K. Tuunainen, Tuure Tuunanen, Fiona Fui-Hoon Nah
(Hidden) Social Influences In Switching Mobile Service Platforms, Virpi K. Tuunainen, Tuure Tuunanen, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
During the past few years, the mobile industry has gone through a radical change from business focusing on excellence in device manufacturing and supply chain management to ecosystems around successful focal players, such as Apple and Google, controlling these service platforms. In order to compete in this environment, these firms need to understand what makes a consumer switch between these mobile service platforms. To that end, we conducted an inductive qualitative study with university students from Finland and USA as subjects (142 altogether), delving into how and why consumers switch mobile phones, and what are the factors affecting their decisions. …
Sampling And Ontologically Pooling Web Images For Visual Concept Learning, Shiai Zhu, Chong-Wah Ngo, Yu-Gang Jiang
Sampling And Ontologically Pooling Web Images For Visual Concept Learning, Shiai Zhu, Chong-Wah Ngo, Yu-Gang Jiang
Research Collection School Of Computing and Information Systems
Sufficient training examples are essential for effective learning of semantic visual concepts. In practice, however, acquiring noise-free training examples has always been expensive. Recently the rapid popularization of social media websites, such as Flickr, has made it possible to collect training exemplars without human assistance. This paper proposes a novel and efficient approach to collect training samples from the noisily tagged Web images for visual concept learning, where we try to maximize two important criteria, relevancy and coverage, of the automatically generated training sets. For the former, a simple method named semantic field is introduced to handle the imprecise and …
Automatic Compositional Verification Of Timed Systems, Shang-Wei Lin, Yang Liu, Jun Sun, Jin Song Dong, Étienne André
Automatic Compositional Verification Of Timed Systems, Shang-Wei Lin, Yang Liu, Jun Sun, Jin Song Dong, Étienne André
Research Collection School Of Computing and Information Systems
Specification and verification of real-time systems are important research topics with crucial applications; however, the so-called state space explosion problem often prevents model checking to be used in practice for large systems. In this work, we present a self-contained toolkit to analyze real-time systems specified using event-recording automata (ERAs), which supports system modeling, animated simulation, and fully automatic compositional verification based on learning techniques. Experimental results show that our tool outperforms the state-of-the-art timed model checker.
Improved Bdd-Based Discrete Analysis Of Timed Systems, Truong Khanh Nguyen, Jun Sun, Yang Liu, Jin Song Dong, Yan Liu
Improved Bdd-Based Discrete Analysis Of Timed Systems, Truong Khanh Nguyen, Jun Sun, Yang Liu, Jin Song Dong, Yan Liu
Research Collection School Of Computing and Information Systems
Model checking timed systems through digitization is relatively easy, compared to zone-based approaches. The applicability of digitization, however, is limited mainly for two reasons, i.e., it is only sound for closed timed systems; and clock ticks cause state space explosion. The former is mild as many practical systems are subject to digitization. It has been shown that BDD-based techniques can be used to tackle the latter to some extent. In this work, we significantly improve the existing approaches by keeping the ticks simple in the BDD encoding. Taking advantage of the ‘simple’ nature of clock ticks, we fine-tune the encoding …
A Non-Parametric Visual-Sense Model Of Images: Extending The Cluster Hypothesis Beyond Text, Kong-Wah Wan, Ah-Hwee Tan, Joo-Hwee Lim, Liang-Tien Chia
A Non-Parametric Visual-Sense Model Of Images: Extending The Cluster Hypothesis Beyond Text, Kong-Wah Wan, Ah-Hwee Tan, Joo-Hwee Lim, Liang-Tien Chia
Research Collection School Of Computing and Information Systems
The main challenge of a search engine is to find information that are relevant and appropriate. However, this can become difficult when queries are issued using ambiguous words. Rijsbergen first hypothesized a clustering approach for web pages wherein closely associated pages are treated as a semantic group with the same relevance to the query (Rijsbergen 1979). In this paper, we extend Rijsbergen’s cluster hypothesis to multimedia content such as images. Given a user query, the polysemy in the return image set is related to the many possible meanings of the query. We develop a method to cluster the polysemous images …
Presynaptic Learning And Memory With A Persistent Firing Neuron And A Habituating Synapse: A Model Of Short Term Persistent Habituation, Kiruthika Ramanathan, Ning Ning, Dhiviya Dhanasekar, Guoqi Li, Luping Shi, Prahlad Vadakkepat
Presynaptic Learning And Memory With A Persistent Firing Neuron And A Habituating Synapse: A Model Of Short Term Persistent Habituation, Kiruthika Ramanathan, Ning Ning, Dhiviya Dhanasekar, Guoqi Li, Luping Shi, Prahlad Vadakkepat
Research Collection School Of Computing and Information Systems
Our paper explores the interaction of persistent firing axonal and presynaptic processes in the generation of short term memory for habituation. We first propose a model of a sensory neuron whose axon is able to switch between passive conduction and persistent firing states, thereby triggering short term retention to the stimulus. Then we propose a model of a habituating synapse and explore all nine of the behavioral characteristics of short term habituation in a two neuron circuit. We couple the persistent firing neuron to the habituation synapse and investigate the behavior of short term retention of habituating response. Simulations show …
Modeling Concept Dynamics For Large Scale Music Search, Jialie Shen, Hwee Hwa Pang, Meng Wang, Shuicheng Yan
Modeling Concept Dynamics For Large Scale Music Search, Jialie Shen, Hwee Hwa Pang, Meng Wang, Shuicheng Yan
Research Collection School Of Computing and Information Systems
Continuing advances in data storage and communication technologies have led to an explosive growth in digital music collections. To cope with their increasing scale, we need effective Music Information Retrieval (MIR) capabilities like tagging, concept search and clustering. Integral to MIR is a framework for modelling music documents and generating discriminative signatures for them. In this paper, we introduce a multimodal, layered learning framework called DMCM. Distinguished from the existing approaches that encode music as an ensemble of order-less feature vectors, our framework extracts from each music document a variety of acoustic features, and translates them into low-level encodings over …
Adaptive Display Power Management For Oled Displays, Kiat Wee Tan, Rajesh Krishna Balan
Adaptive Display Power Management For Oled Displays, Kiat Wee Tan, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
Mobile gaming has become increasingly popular in the past few years with the proliferation of smartphones that have the increased CPU, memory, and network (3.5G etc.) capabilities to support a vast range of interesting games. In addition, these phones also have high quality displays, such as Organic Light Emitting Diodes (OLED) displays, that allow the intricate details in games to be shown in vivid detail to end users. Unfortunately, these displays tend to consume a lot of energy which in turn limits the amount of time that a user can spend actually playing games on these devices. In this paper, …
Embracing Analytics For A Better Competitive Edge, Tin Seong Kam
Embracing Analytics For A Better Competitive Edge, Tin Seong Kam
Research Collection School Of Computing and Information Systems
No abstract provided.
Using Monterey Phoenix To Formalize And Verify System Architectures, Jiexin Zhang, Yang Liu, Mikhail Auguston, Jun Sun, Jin Song Dong
Using Monterey Phoenix To Formalize And Verify System Architectures, Jiexin Zhang, Yang Liu, Mikhail Auguston, Jun Sun, Jin Song Dong
Research Collection School Of Computing and Information Systems
Modeling and analyzing software architectures are useful for helping to understand the system structures and facilitate proper implementation of user requirements. Despite its importance in the software engineering practice, the lack of formal description and verification support hinders the development of quality architectural models. In this work, we develop an approach for modeling and verifying software architectures specified using Monterey Phoenix (MP) architecture description language. Firstly, we formalize the syntax and operational semantics for MP. This language is capable of modeling system and environment behaviors based on event traces, as well as supporting different architecture composition operations and views. Secondly, …
Probabilistic Model Checking Multi-Agent Behaviors In Dispersion Games Using Counter Abstraction, Jianye Hao, Songzheng Song, Yang Liu, Jun Sun, Lin Gui, Jin Song Dong, Ho-Fung Leung
Probabilistic Model Checking Multi-Agent Behaviors In Dispersion Games Using Counter Abstraction, Jianye Hao, Songzheng Song, Yang Liu, Jun Sun, Lin Gui, Jin Song Dong, Ho-Fung Leung
Research Collection School Of Computing and Information Systems
Accurate analysis of the stochastic dynamics of multi-agent system is important but challenging. Probabilistic model checking, a formal technique for analysing a system which exhibits stochastic behaviors, can be a natural solution to analyse multi-agent systems. In this paper, we investigate this problem in the context of dispersion games focusing on two strategies: basic simple strategy (BSS) and extended simple strategies (ESS). We model the system using discrete-time Markov chain (DTMC) and reduce the state space of the models by applying counter abstraction technique. Two important properties of the system are considered: convergence and convergence rate. We show that these …
An Economic Analysis Of The Online Counterfeit Market And The Impact Of Anti-Counterfeit Technology, Xiong Zhang, Zhiling Guo, Wei Thoo Yue
An Economic Analysis Of The Online Counterfeit Market And The Impact Of Anti-Counterfeit Technology, Xiong Zhang, Zhiling Guo, Wei Thoo Yue
Research Collection School Of Computing and Information Systems
Counterfeiting causes hundreds of billions dollars of losses around the world every year. Due to the growing prominence of online commerce, the seriousness of the situation could soon become much worse. Hence, reaching a clear understanding of the fundamental economic incentives behind this practice is of vital importance. In this paper, we investigate a problem within which a firm selling a counterfeit product engages in price competition with a firm that sells an authentic product to a population of heterogeneous consumers. An online intermediary acts as the facilitator of both firms’ transactions and may consequently be liable for any counterfeit …
Lagrangian Relaxation Techniques For Scalable Spatial Conservation Planning, Akshat Kumar, Xiaojian Wu, Shlomo Zilberstein
Lagrangian Relaxation Techniques For Scalable Spatial Conservation Planning, Akshat Kumar, Xiaojian Wu, Shlomo Zilberstein
Research Collection School Of Computing and Information Systems
We address the problem of spatial conservation planning in which the goal is to maximize the expected spread of cascades of an endangered species by strategically purchasing land parcels within a given budget. This problem can be solved by standard integer programming methods using the sample average approximation (SAA) scheme. Our main contribution lies in exploiting the separable structure present in this problem and using Lagrangian relaxation techniques to gain scalability over the flat representation. We also generalize the approach to allow the application of the SAA scheme to a range of stochastic optimization problems. Our iterative approach is highly …
On-Line Portfolio Selection With Moving Average Reversion, Bin Li, Steven C. H. Hoi
On-Line Portfolio Selection With Moving Average Reversion, Bin Li, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
On-line portfolio selection has attracted increasing interests in machine learning and AI communities recently. Empirical evidences show that stock's high and low prices are temporary and stock price relatives are likely to follow the mean reversion phenomenon. While the existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied in some real datasets, leading to poor performance when the assumption does not hold. To overcome the limitation, this article proposes a multiple-period mean reversion, or so-called Moving Average Reversion (MAR), and a …
Exact Soft Confidence-Weighted Learning, Jialei Wang, Steven C. H. Hoi
Exact Soft Confidence-Weighted Learning, Jialei Wang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
In this paper, we propose a new Soft Confidence-Weighted (SCW) online learning scheme, which enables the conventional confidence-weighted learning method to handle non-separable cases. Unlike the previous confidence-weighted learning algorithms, the proposed soft confidence-weighted learning method enjoys all the four salient properties: (i) large margin training, (ii) confidence weighting, (iii) capability to handle non-separable data, and (iv) adaptive margin. Our experimental results show that the proposed SCW algorithms significantly outperform the original CW algorithm. When comparing with a variety of state-of-the art algorithms (including AROW, NAROW and NHERD), we found that SCW generally achieves better or at least comparable predictive …
Fast Bounded Online Gradient Descent Algorithms For Scalable Kernel-Based Online Learning, Peilin Zhao, Jialei Wang, Pengcheng Wu, Rong Jin, Steven C. H. Hoi
Fast Bounded Online Gradient Descent Algorithms For Scalable Kernel-Based Online Learning, Peilin Zhao, Jialei Wang, Pengcheng Wu, Rong Jin, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Kernel-based online learning has often shown state-of-the-art performance for many online learning tasks. It, however, suffers from a major shortcoming, that is, the unbounded number of support vectors, making it non-scalable and unsuitable for applications with large-scale datasets. In this work, we study the problem of bounded kernel-based online learning that aims to constrain the number of support vectors by a predefined budget. Although several algorithms have been proposed in literature, they are neither computationally efficient due to their intensive budget maintenance strategy nor effective due to the use of simple Perceptron algorithm. To overcome these limitations, we propose a …
Online Kernel Selection: Algorithms And Evaluations, Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Jinfeng Yi, Steven C. H. Hoi
Online Kernel Selection: Algorithms And Evaluations, Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Jinfeng Yi, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Kernel methods have been successfully applied to many machine learning problems. Nevertheless, since the performance of kernel methods depends heavily on the type of kernels being used, identifying good kernels among a set of given kernels is important to the success of kernel methods. A straightforward approach to address this problem is cross-validation by training a separate classifier for each kernel and choosing the best kernel classifier out of them. Another approach is Multiple Kernel Learning (MKL), which aims to learn a single kernel classifier from an optimal combination of multiple kernels. However, both approaches suffer from a high computational …
Using Interactive Evolutionary Computation (Iec) With Validated Surrogate Fitness Functions For Redistricting, Christine Chou, Steven Kimbrough, John Sullivan-Fedock, C. Jason Woodard, Frederic H. Murphy
Using Interactive Evolutionary Computation (Iec) With Validated Surrogate Fitness Functions For Redistricting, Christine Chou, Steven Kimbrough, John Sullivan-Fedock, C. Jason Woodard, Frederic H. Murphy
Research Collection School Of Computing and Information Systems
We describe a novel use of evolutionary computation to discover good districting plans for the Philadelphia City Council. We discovered 116 distinct, high quality, legally valid plans. These constitute a rich resource for stakeholders to base deliberation. This raises the issue of how to deal with large numbers of plans, especially with the aim of avoiding gerrymandering and promoting fairness. Interactive Evolutionary Computation (IEC) is a natural approach here, if practicable. The paper proposes development of Validated Surrogate Fitness (VSF) functions as a workable and generalizable form of IEC.