Machine learning (ML)-enabled systems like autonomous driving systems (ADSs) face challenges meeting safety and performance requirements in diverse environments, especially in resource-constrained, latency-sensitive edge-cloud settings. These challenges often arise from the ML models’ limitations, including poor generalization to unseen conditions. Static ML models often struggle to generalize to unseen scenarios, particularly under the latency and resource constraints of edge-cloud infrastructure. Adaptive algorithms using ML system switching have been proposed, but existing approaches frequently lack generalizability, support for common black-box systems, and effective use of distributed edge-cloud resources. This paper presents a novel adaptive ML-enabled edge-cloud system framework to address these shortcomings. Our framework combines cloud-based pre-runtime analysis, which leverages simulation for behavioral understanding and scenario-to-system mapping, with collaborative edge-cloud runtime adaptation featuring dynamic ML model switching. It supports black-box systems and aims to balance safety and efficiency by utilizing appropriate edge and cloud resources situationally. Preliminary CARLA-based evaluation of the edge runtime component suggests our framework can potentially improve the safety-efficiency trade-off compared to single-model ADSs in some scenarios. Moreover, extensive experiments using the MetaDrive simulator with 100,000 randomized driving scenarios demonstrate that the adaptive system improves safety by 2.6% while doubling computational efficiency compared to a single-model baseline. These results validate the framework’s scalability and the feasibility of data-driven scenario–system mapping for adaptive ML-enabled autonomous systems operating across edge and cloud environments.

In recent years, the tabular generative model has emerged as a major research area across finance applications (e.g., fraud detection), especially under circumstances where original data are unavailable due to financial regulations such as the General Data Protection Regulation (GDPR). However, there is a notable scarcity of research on tabular generative models that directly handle the three characteristics of Financial Fraud Detection (FFD) data toward more effective FFD: (a) extreme class imbalance, (b) high data sparsity, and (c) a large number of attributes with non-normal distribution. Through a preliminary study, we confirmed that the performance of existing tabular generative models declines as the intensity of the three characteristics increases. To address these challenges, we propose novel graph-theoretical tabular generative models for tabular data generation, which are specialized for FFD. More specifically, we implement three variants of our method— SeparateGGM , SignedGGM , and HybridGGM —which are tailored to capture class-wise structure, signed inter-class relationships, and their integration, respectively. Our models jointly leverage several graph-theoretical methodologies, including graph neural network-based feature augmentation, graph topology and connectivity analysis, and new graph centrality indicators that we design in this work. Especially, through the graph analysis, we empirically find that certain hyperparameter values (i.e., positive and negative K ratios in separate and signed K-NN graphs), which make the related graph measurements approximately the highest points, consistently yield the highest detection accuracy, thus facilitating the selection of effective graph settings. Experimental results show that our method outperforms seven different competing methods across three popular FFD benchmarks and one private dataset. Finally, we plan to deploy SignedGGM , the best efficient variant of our method, and HybridGGM-A , the best-performing variant of our method, in a real-world financial application in the Financial Security Institute (FSI), one of South Korea’s financial government agencies.

We present OrchestML, a prototype tool for continuous integration of machine learning service pipelines. Modern ML systems increasingly depend on composing specialized services, yet identifying, integrating, and maintaining them across heterogeneous repositories remains challenging. OrchestML addresses this by (i) discovering services through cross-repository semantic search, (ii) generating executable blueprints via a staged LLM-powered pipeline, and (iii) adapting deployed pipelines through performance monitoring and automated recomposition. An interactive web interface enables users to explore composition alternatives, validate service dependencies through graph visualization, and deploy pipelines with integrated monitoring. A case study in robotic navigation shows that OrchestML can automatically detect performance degradation and trigger recomposition, improving execution efficiency by $20 \%$. OrchestML is open sourced at https://github.com/gurkhaman/OrchestML, and the demo video is provided at https://youtu.be/4Z5YMqJU1TA.

The development of effective fraud detection systems (FDS) is hindered by strict data privacy regulations that prevent centralized data sharing. Federated learning (FL) has emerged as a promising alternative, enabling collaborative model training without exposing sensitive data. While FL has been explored in the healthcare domain, research on its application to financial fraud detection remains relatively limited. Specifically, FL research on real-world banking fraud types-with detailed customer, account, and transaction data-remains underexplored. We present the first empirical study of federated gradient boosting models for financial fraud detection in the banking sector, motivated by their superior performance over deep learning models on tabular fraud data. We evaluate and compare four representative federated gradient boosting models using both a private multi-fraud banking dataset from the Financial Security Institute (FSI) and a publicly available banking dataset, under various scenarios. Key findings include the consistent superiority of FedXGBBagging (a federated gradient boosting model), general vulnerability to data quantity skew, performance instability under bank join/dropout, and limitations in detecting localized banking fraud types such as ATM skimming. The findings from our empirical study highlight challenges and design considerations for deploying FL-based FDSs in the banking sector.

Notably, current intelligent transportation systems rely heavily on accurate traffic forecasting and swift inference provision to make timely decisions. While Graph Convolutional Networks (GCNs) have shown benefits in modeling complex traffic dependencies, the existing GCN-based approaches cannot fully extract and fuse multi-granular spatiotemporal features across various spatial and temporal scales sufficiently in a complete manner, proven to yield less accurate results. As extracting multi-granular features across scales has been a promising strategy across domains such as computer vision, natural language processing, and time-series forecasting, pioneering studies have attempted to leverage a similar mechanism for spatiotemporal traffic data mining. However, additional feature extraction branches introduced in prior studies critically increased model complexity and extended inference time, making it challenging to provide fast forecasts. In this paper, we propose MultiGran-STGCNFog, an efficient fog distributed inference system with a novel traffic forecasting model that employs multi-granular spatiotemporal feature fusion on generated dynamic traffic graphs to fully capture interdependent traffic dynamics. The proposed scheduling algorithm GA-DPHDS, optimizing layer execution order and layer-device scheduling scheme simultaneously, contributes to considerable inference throughput improvement by coordinating heterogeneous fog devices in a pipelined manner. Extensive experiments on real-world datasets demonstrate the superiority of the proposed method over selected GCN baselines.

Abstract The scope of the Internet of Things (IoT) environment has been expanding from private to public spaces, where selecting the most appropriate service by predicting the service quality has become a timely problem. However, IoT services can be physically affected by (1) uncertain environmental factors such as obstacles and (2) interference among services in the same environment while interacting with users. Using the traditional modeling-based approach, analyzing the influence of such factors on the service quality requires modeling efforts and lacks generalizability. In this study, we propose Learning Physical Environment factors based on the Attention mechanism to Select Services for UsERs (PLEASSURE), a novel framework that selects IoT services by learning the uncertain influence and predicting the long-term quality from the users’ feedback without additional modeling. Furthermore, we propose fingerprint attention that extends the attention mechanism to capture the physical interference among services. We evaluate PLEASSURE by simulating various IoT environments with mobile users and IoT services. The results show that PLEASSURE outperforms the baseline algorithms in rewards consisting of users’ feedback on satisfaction and interference.

The Internet of Things (IoT) has gained more attention for enhancing users’ daily lives in public spaces by providing services using shareable devices. However, uncertain factors and other services in the environment may affect the service severely, resulting in users’ low satisfaction. Based on multiagent reinforcement learning and cluster-based federated learning, autonomous service agents may learn the complex influence of the factors from user feedback without sophisticated modeling and detection processes. However, conventional approaches are limited in dealing with multiple clustering dimensions of service agents and dynamic environmental contexts affecting the agents. In this work, we propose the multidimension and multiagent federated reinforcement learning (MultiFedRL) for efficient training of service agents in public IoT environments. First, we suggest a parallel structure of neural networks for multiple clustering dimensions to share parameters independently, solving the limitation of conventional cluster-based federated learning. Second, we suggest an environment-centric learnable communication protocol for the agents to summarize and interpret physical contexts consisting of static characteristics and dynamic states. To evaluate MultiFedRL, we developed a simulation framework for IoT services provided to mobile users in public spaces, imitating the user-service interaction based on crucial physics phenomena. Experimental results show that MultiFedRL increases user satisfaction by 82.9% and training efficiency by 24.5% compared to state-of-the-art cluster-based federated learning.

As the number of IoT devices and the volume of data increase, distributed computing systems have become the primary deployment solution for large-scale Internet of Things (IoT) environments. Federated learning (FL) is a collaborative machine learning framework that allows for model training using data from all participants while protecting their privacy. However, traditional FL suffers from low computational and communication efficiency in large-scale hierarchical cloud-edge collaborative IoT systems. Additionally, due to heterogeneity issues, not all IoT devices necessarily benefit from the global model of traditional FL, but instead require the maintenance of personalized levels in the global training process. Therefore we extend FL into a horizontal peer-to-peer (P2P) structure and introduce our P2PFL framework: efficient peer-to-peer federated learning for users (EPFLU). EPFLU transitions the paradigms from vertical FL to a horizontal P2P structure from the user perspective and incorporates personalized enhancement techniques using private information. Through horizontal consensus information aggregation and private information supplementation, EPFLU solves the weakness of traditional FL that dilutes the characteristics of individual client data and leads to model deviation. This structural transformation also significantly alleviates the original communication issues. Additionally, EPFLU has a customized simulation evaluation framework, and uses the EUA dataset containing real-world edge server distribution, making it more suitable for real-world large-scale IoT. Within this framework, we design two extreme data distribution scenarios and conduct detailed experiments of EPFLU and selected baselines on the MNIST and CIFAR-10 datasets. The results demonstrate that the robust and adaptive EPFLU framework can consistently converge to optimal performance even under challenging data distribution scenarios. Compared with the traditional FL and selected P2PFL methods, EPFLU achieves communication time improvements of 39% and 16% respectively.

The emergence of fog computing has shown promising results for reducing network latency and congestion in the cloud. In this environment, effective autoscaling to handle spatio-temporal load bursts in geographically distributed and resource-constrained fog nodes has become a timely problem. A typical strategy for autoscaling is based on centralized monitoring of the fog nodes and the deployed service instances. However, centralized collection and analysis of the metrics for autoscaling can become infeasible with the increasing number of fog nodes. Moreover, the dynamic and fluctuating characteristics of the fog nodes make effective autoscaling challenging in fog computing environments. In this work, we propose HiDRA, a Hierarchical Decentralized Autoscaler that scales and places microservice instances based on multi-agent reinforcement learning. In HiDRA, agents are divided into scaling and placement agents that collaborate with each other to effectively handle spatio-temporal load bursts in fog computing. These Deep Q-Network-based autoscaling agents are trained solely based on their regional observations at runtime, eliminating the need for a centralized collection of metrics. We evaluated HiDRA in multiple simulated fog environments created using a real-world dataset. The environments were divided into three levels of sparsity, each consisting of 20, 15, and 10 initial instances and unstable nodes. The result shows that comparatively by ratio against the baseline, HiDRA increased the average request success rate by 10.7%, 16.4%, and 36.7% and reduced the number of created instances by 12.3%, 15.9%, and 16.8% in environments with 20, 15, and 10 initial instances and unstable nodes, respectively.

Pre-trained models (PTMs) have revolutionized machine learning by significantly enhancing reusability and reducing resources required for model training. Despite their advantages, selecting appropriate PTMs for specific system requirements remains difficult due to application heterogeneity and variable task performance. This has led to the proposal of PTM compositions to enhance capabilities beyond individual models, with an on-the-fly approach being applied further to meet dynamic requirements. However, composing PTMs on-the-fly can result in objective mismatch, leading to inefficiencies and errors in constituent PTMs. This necessitates the timely and efficient replacement of underperforming PTMs. The process is a major challenge due to the vast number of candidates and the extensive time required for evaluation. Therefore, we propose the Sample-Infer-Predict framework for efficient on-the-fly PTM replacement which comprises three phases: sampling, inference, and prediction. First, our novel Density and Diversity sampling algorithm efficiently selects representative user inputs. Second, the inference phase evaluates candidate PTMs from model hubs for the prediction dataset. Third, the prediction phase utilizes the dataset to predict the optimal PTM replacements. We evaluate our methodology using a vehicle detection PTM composition and a dataset of 5849 vehicle images, focusing on the efficiency of the replacement process, the quality of the meta-predictions, and the effectiveness of our sampling technique. Our approach reduces replacement time (155s vs. 28293s), achieves high precision@k values, and lowers mean absolute error values compared to other sampling techniques.

While the emergence of fog computing has shown promising results for reducing the network latency and congestion in the cloud, effective autoscaling to handle spatio-temporal bursts of load has become a timely problem. In this work, we propose HiDRA, a Hierarchical Decentralized Reactive Autoscaler that scales and places microservice instances based on multi-agent reinforcement learning. In HiDRA, the agents are divided into scaling and placement agents that collaborate with each other to effectively handle spatio-temporal bursts of load in fog computing. These hierarchical agents are trained solely based on their regional observations at runtime, eliminating the need for a centralized collection of metrics. We evaluated HiDRA in 20 simulated fog environments to show that HiDRA reduces the created number of instances by 36.70% while resulting in similar scaling performance in terms of response success rate.

In the context of the recent emergence of the Internet of Things (IoT), human users and IoT-based services are interacting via physical effects, such as light and sound. Therefore, it is necessary to consider the quality of the delivery of physical effects to users by IoT devices for selecting services in IoT environments. However, traditional service-selection algorithms focus primarily on the network-level Quality of Service (QoS), such as latency and throughput. In this study, we improve on the visual-service effectiveness metric developed in our previous work to measure the effectiveness of the personalized delivery of physical effects of visual services to users by considering user- and application-specific factors. We evaluate the metric by conducting a user study, and the results show that the metric reflects users’ perceived effectiveness with high accuracy. We also investigate the use of virtual reality (VR) to imitate physical environments for efficient evaluation of the metric. Based on this metric, we develop a dynamic effect-driven output-service selection agent (DEOSA) that selects output services dynamically by considering the effectiveness of service-effect delivery. By adopting a state-of-the-art reinforcement-learning algorithm, DEOSA can learn the optimal policy for selecting output services that can be generalized to various environments. We evaluate DEOSA in simulated IoT environments and show that it can learn the optimal policy successfully; it generally performs better than traditional greedy algorithms in terms of the visual service effectiveness metric and the replacement overhead in randomly generated test environments.

With the increasing number of Web of Things devices, the network and processing delays in the cloud have also increased. As a solution, fog computing has emerged, placing computational resources closer to the user to lower the communication overhead and congestion in the cloud. In fog computing systems, microservices are deployed as containers, which require an orchestration tool like Kubernetes to support service discovery, placement, and recovery. A key challenge in the orchestration of microservices is automatically scaling the microservices in case of an unpredictable burst of load. In cloud computing, a centralized autoscaler can monitor the deployed microservice instances and make scaling actions based on the monitored metric values. However, monitoring an increasing number of microservices in fog computing can cause excessive network overhead and thereby delay the time to scaling action. We propose DESA, a fully DEcentralized Self-adaptive Autoscaler through which microservice instances make their own scaling decisions, cloning or terminating themselves through self-monitoring. We evaluate DESA in a simulated fog computing environment with different numbers of fog nodes. Furthermore, we conduct a case study with the 1998 World Cup website access log, examining DESA’s performance in a realistic scenario. The results show that DESA successfully reduces the scaling reaction time in large-scale fog computing systems compared to the centralized approach. Moreover, DESA resulted in a similar maximum number of instances and lower average CPU utilization during bursts of load.

Existing conversational approaches for Internet of Things (IoT) service mashup do not support modification because of the usability challenge, although it is common for users to modify the service mashups in IoT environments. To support the modification of IoT service mashups through conversational interfaces in a usable manner, we propose the conversational mashup modification agent (CoMMA). Users can modify IoT service mashups using CoMMA through natural language conversations. CoMMA has a two-step mashup modification interaction, an implicature-based localization step, and a modification step with a disambiguation strategy. The localization step allows users to easily search for a mashup by vocalizing their expressions in the environment. The modification step supports users to modify mashups by speaking simple modification commands. We conducted a user study and the results show that CoMMA is as effective as visual approaches in terms of task completion time and perceived task workload for modifying IoT service mashups.

In highly dynamic IoT environments such as Vehicle-to-Everything (V2X), discovering and providing Internet of Things (IoT) services to users is a challenging problem because the environments alter too quickly and unpredictably. For a fast and effective service discovery in a highly dynamic environment, we propose a cached service registry on mobile entities along with new metrics for evaluating the effectiveness of service discoveries. We conducted an experiment on a simulated V2X environment, and showed that both the rate of finding accessible services and the utilization of services can be improved comparing to the traditional service discovery method.

In highly dynamic IoT environments, the connection statuses of IoT resources and the availability of IoT-based services change frequently. Therefore, to successfully build distributed service registries for managing and finding the information about available services in an effective manner, it is crucial to minimize the overhead of message exchanges between registries and to reduce the time overhead for identifying the capabilities of available IoT resources and the services that can be provided by utilizing these capabilities. In this paper, we propose a lightweight messaging approach that uses hierarchical Bloom filters to efficiently represent service information to be exchanged and managed by distributed service registries for IoT environments with high mobility. We also propose a method for serializing the dimensions of a Bloom-filter-encoded search space. We conducted experiments to demonstrate the improvement in the service discovery performance, the reduction in message traffic among service registries, and the decrease in the latency when synchronizing distributed service registries.

The functions, capabilities, and effects produced by the application services of cyber physical systems (CPS) are usually consumed by users performing their daily activities in a variety of environmental conditions. Thus, it is critical to ensure that those systems neither interfere with human activities nor harm the users involved. In this paper, we propose a framework for testing and verifying the safety and reliability of CPS services from the perspectives of CPS environments and users. The framework provides an environmentaware testing method by which the efficiency of testing CPS services can be improved by prioritizing CPS environments and by applying machinelearning techniques. The framework also includes a metric by which we can automate the test of the most effective services that deliver effects from physical devices to users. Additionally, the framework provides a computational model that assesses mental workloads to test whether a CPS service can cause cognitive depletion or contention problems for users. We conducted a series of experiments to show the effectiveness of the proposed approaches for ensuring the safety and reliability of CPS application services during the development and operation phases.

Recent advances in Internet of Things (IoT) technologies have encouraged web services to expand their provision boundary to physical environments by utilizing IoT devices. IoT services that generate and deliver physical effects to users via a space utilize such IoT devices as media to interact within a physical environment. Existing studies on dynamic service selection have only considered network-level quality of service (QoS) attributes, which cannot be used to evaluate the quality of the delivery of physical effects from a user perspective. Furthermore, to provide the services in a continuous manner, a dynamic selection of physical media is essential. Herein we propose a new metric called visual service effectiveness to evaluate how well a visual effect, generated using an IoT device as a medium, can be delivered to a user. Based on this metric, we also propose an effect-driven dynamic medium selection agent (EDMS-Agent) that conducts medium selection to maximize the visual service effectiveness during runtime and can be trained using reinforcement learning algorithms. We evaluated our EDMS-Agent by conducting several experiments in simulated IoT environments. The results show that simple distance-based metric is insufficient to measure the quality of physical effects in the user's perspective, and EDMS-Agent performs better than the baselines in terms of effectiveness, by learning the optimal policy of selecting media.

When testing client-side web applications, it is important to consider different web-browser environments. Different properties of these environments such as web-browser types and underlying platforms may cause a web application to exhibit different types of failures. As web applications evolve, they must be regression tested across these different environments. Because there are many environments to consider this process can be expensive, resulting in delayed feedback about failures in applications. In this work, we propose six techniques for providing a developer with faster feedback on failures when regression testing web applications across different web-browser environments. Our techniques draw on methods used in test case prioritization; however, in our case we prioritize web-browser environments, based on information on recent and frequent failures. We evaluated our approach using four non-trivial and popular open-source web applications. Our results show that our techniques outperform two baseline methods, namely, no ordering and random ordering, in terms of the cost-effectiveness. The improvement rates ranged from −12.24% to 39.05% for no ordering, and from −0.04% to 45.85% for random ordering.

There are an increasing number of public Internet of Things (IoT) devices installed in urban environments, with which users can perform a wide variety of tasks. Owing to the nature of public spaces, such IoT devices must support groups of users rather than just individuals. However, because the type and quality of IoT devices in public environments varies, it may be difficult for groups of users to recognize the opportunities to perform tasks. Moreover, group users are often new to a certain public place, and have not previously performed tasks in IoT-enriched public spaces. In this paper, we propose a two-phase task recommendation approach for groups of IoT users in public environments. In the first phase, we employ a random walk with restart (RWR) algorithm to overcome the problem of sparse historical data for the performance of user tasks in public IoT environments. The second phase predicts a set of operations (IoT device functionalities) that are most appropriate for each candidate task. In this phase, to more effectively predict IoT operations for a user task we consider the contextual semantics of users via a classification model. We evaluate our approach using real-world datasets collected from practical IoT testbed environments. In addition, we show that an appropriate set of task operations can be predicted effectively by considering task types and contextual semantics.

When a user of a computer system is performing more than one task at the same time, her error rate increases drastically. In any system this is a critical issue, since the goals of the tasks are not likely to be met. In that sense, the purpose of mental workload assessment is to estimate the mental demand of tasks to take action according to that, avoiding execution errors. In this paper we study two techniques of mental workload assessment, physiological signals and simulation models of mental behavior with the ACT-R cognitive architecture. The contributions of this study are in two folds: validate a positive correlation among physiological and simulated data and, to develop a supervised model of classification with a cost-sensitive feature selection algorithm using the ACT-R simulated data as an input of the model. Results show a positive, significant correlation among the two data sources, and that the model selects features of less cost and classify better than a baseline approach with 93.1% accuracy in average.

Regression testing in continuous integration development environments must be cost-effective and should provide fast feedback on test suite failures to the developers. In order to provide faster feedback on failures to developers while using computing resources efficiently, two types of regression testing techniques have been developed: Regression Testing Selection (RTS) and Test Case Prioritization (TCP). One of the factors that reduces the effectiveness of the RTS and TCP techniques is the inclusion of test suites that fail only once over a period. We propose an approach based on Bloom filtering to exclude such test suites during the RTS process, and to assign such test suites with a lower priority during the TCP process. We experimentally evaluate our approach using a Google dataset, and demonstrate that cost-effectiveness of the proposed RTS and TCP techniques outperforms the state-of-the-art techniques.

Recommender systems can be used to assist groups of users to select services in Internet of Things (IoT)-enriched environments. However, aggregating the preferences of the individual users of a group, which is generally used in group recommendation, is not appropriate for IoT environments, where the user groups' preferences for IoT-based services differ significantly from those of individual users. In this paper, we propose a user-based collaborative filtering approach that considers member organization for a new user group. We select neighbor user groups that are similar to the new group based on combinations of member organization-based group similarity (MOGS) metrics such as the group size-based, common member-based, and member preference-based metrics. We conduct experiments to evaluate our approach using real-world datasets collected from practical IoT testbed environments. The results demonstrate that the proposed approach is effective in improving the performance and stability of service recommendations in IoT environments regardless of the locational characteristics.

The emergence of computing power and the abundance of data have made it possible to assist human decisions, especially in the stock markets, in which the ability to predict future values would lower the risk of investing. In this paper, we present a new approach for identifying the predictive power of public emotions extracted from various sections of daily news articles on the movements of stock market indices. The approach utilizes the results of a lexicon emotion analysis conducted on crowd-annotated news to extract various types of public emotions from daily news articles. We also propose a model and an analysis method to score news articles regarding public emotions, and to identify which news sections and emotions cause movements in a stock market index. The results of an experiment conducted with 24,763 news articles show that some types of public emotions are significantly correlated with changes in the trading volume and the closing price of a stock market.

An emerging issue in urban computing environments is the seamless selection, composition, and delivery of user-centric services that run over what is known as the Internet of Things (IoT). This challenge is about enabling services actuated by IoT devices to be delivered spontaneously from the perspective of users. To accomplish this goal, we propose the Service-Oriented Internet of Things (SoIoT), a user-centric IoT-based service framework, which integrates services that utilize IoT resources in an urban computing environment. This framework provides a task-oriented computing approach that enables the composition of IoT-based services in a spontaneous manner to accomplish a user task. Tasks can also be recommended to users based on the available IoT resources in an environment and on the contextual knowledge that is represented and managed in social, spatial, and temporal aspects. These tasks are then bound to a set of service instances and performed in a distributed manner. This final composition ensures the Quality of Service (QoS) requirements of the tasks and is assigned to multiple client devices for the efficient utilization of IoT resources. We prove the practicality of our approach by showing a real-case service scenario implemented in our IoT-based test-bed as well as experimental results.

In task-oriented service computing, a user's computing goal is modeled and represented as a task, which is composed of activities that are performed by accessing service instances in a local environment. The abstract service requirements specified in an activity of a task are resolved and bound to service instances dynamically in runtime. When there are many candidate services that provide similar capabilities for a task, it is essential to consider quality of service (QoS) such as response time, latency, and availability to determine which service instances to use. Finding a service composition that meets the optimal level of quality is a well-known NP-hard problem-the time complexity for task-level (global) optimization increases exponentially as the number of services and the number of quality attributes increase. Although it is possible to use a heuristic approach that shows a reasonable response time with a certain level of service quality, this strategy often fails when there are hard QoS constraints that need to be considered in the task level. In this paper, to overcome this limitation, we propose an adaptive method of selecting services based on the hardness of QoS constraints. The basic idea is to sample services that represent a specific quality-value range. The quality-value range of candidate services is divided into smaller sub-ranges in which representative services are sampled and evaluated. At this time, the size of the QoS sub-ranges is determined adaptably based on the hardness of the QoS constraints. In a QoS sub-range, candidate services may have a similar QoS value for a quality attribute. We calculate the utility of candidate services in a QoS sub-range and sample the highest utility service. This process of sampling services and evaluating their utility value is repeated until it makes a composite service that has the highest level of global utility for a task. Our experiment results show that the proposed approach effectively improves the success rate of service composition while achieving a certain level of global optimality and maintaining a reasonable level of performance. Our approach shows up to 80 percent improvement in success rate in comparison to the existing heuristic approaches.

As various types of Internets of Things (IoT) are deployed in a wide range of areas, the need arises to utilize various IoT resources dynamically to accomplish user tasks. We call this environment an urban-scale IoT environment, where various IoT resources that are necessary to accomplish user tasks are directly connected to each other via users' mobile devices, such as their smart phones. IoT resources are utilized as resources with which to run a composite service that supports user tasks. In this urban-scale IoT environment, it is essential to create efficient binding between a service and an IoT resource so as to execute a composite service for a task successfully. In this paper, we propose a service resource allocation approach which minimizes data transmissions between users' mobile devices and which effectively deal with the constraints of these types of environments. We transformed the resource allocation problem into a variant of the degree-constrained minimum spanning tree problem and applied a genetic algorithm to reduce the time needed to produce a near-optimal solution. We also defined a fitness function and an encoding scheme to apply the genetic algorithm in an efficient manner. The proposed approach shows a 97% success rate on average when used to find near-optimal solutions. In addition, it takes significantly less time than the brute force approach.

Open Source Software (OSS) has become an important environment where developers can create, exchange, and improve reusable software assets by collaborating with other developers. Although developers may find useful software assets to reuse from OSS for their projects, they usually experience difficulties in solving problems that occur while integrating the assets to their own software. We investigated data from major open source environments such as Sourceforge.net and GitHub, and learned that there is a common pattern of solving reuse-related problems in OSS. To analyze the pattern in detail, we have developed an ontological model to formally represent the symptoms and causes of the reuse-related problems, and the correlations between them. Based on this model, we collected data from Sourceforge.net, and built a knowledge base for the most common problem type. We extracted the core types of symptoms and causes for the problem type and calculated the number of correlations between the types of symptoms and causes. We found that there exist correlations between the symptoms and causes that are extracted from the discussion threads for the problem type, and about 60% of them are statistically significant. We also conducted a study to understand the effective timing of recommending solutions to the developers by analyzing the recall rates of finding the causes of the problems in a timeline. We figured that most of the important causes of a problem are discussed at the beginning of the forum discussion. This leads us to the conclusion that recommending the causes of a problem early by using our knowledge framework may help developers spend less amount of time to solve the problem (around 50% less time than solving the problem without using our framework).

In regression testing, running all a system's test cases can require a great deal of time and resources. Test case prioritization (TCP) attempts to schedule test cases to achieve goals such as higher coverage or faster fault detection. While code coverage-based approaches are typical in TCP, recent work has explored the use of additional information to improve effectiveness. In this work, we explore the use of Information Retrieval (IR) techniques to improve the effectiveness of TCP, particularly for testing infrequently tested code. Our approach considers the frequency at which elements have been tested, in additional to traditional coverage information, balancing these factors using linear regression modeling. Our empirical study demonstrates that our approach is generally more effective than both random and traditional code coverage-based approaches, with improvements in rate of fault detection of up to 4.7%.

In this paper, we present an opportunistic service recommendation scheme in a place with smart objects to provide users with appropriate composite services to accomplish their task goals. The proposed scheme infers possible tasks from interactions that people have experienced with smart objects including other places that are similar to a given place. The proposed scheme then evaluates the candidate tasks by measuring quality satisfaction of each task against given QoS constraints. We test the proposed scheme on our three smart testbeds where various smart objects equipped. Experiment results show that the proposed scheme finds potential tasks in the testbeds in a reasonable time.

Software complexity has increased the need for automated software testing. Most research on automating testing, however, has focused on creating test input data. While careful selection of input data is necessary to reach faulty states in a system under test, test oracles are needed to actually detect failures. In this work, we describe Dodona, a system that supports the generation of test oracles. Dodona ranks program variables based on the interactions and dependencies observed between them during program execution. Using this ranking, Dodona proposes a set of variables to be monitored, that can be used by engineers to construct assertion-based oracles. Our empirical study of Dodona reveals that it is more effective and efficient than the current state-of-the-art approach for generating oracle data sets, and can often yield oracles that are almost as effective as oracles hand-crafted by engineers without support.

As emerging paradigms such as service-oriented computing and ubiquitous computing become combined, end-users are now being provided with a myriad of services to utilize smart objects to achieve their goals. The most promising standard to utilize these services is WS-BPEL, which employs centralized coordination for simpler management of interaction and synchronization. However, centralized approaches suffer from scalability and heterogeneity issues as well as inefficiency, especially when the system is managed across different entities. Moreover, it is quite unrealistic to assume that one client device has permissions to access all available operations of smart objects and can also support different types of required communication interfaces. Hence, in this paper, we propose a novel distributed coordination scheme that helps end-users collaborate more efficiently to achieve their common goals. Unlike traditional distributed coordination methods, which are limited to static environments, the proposed scheme incorporates dynamic ubiquitous computing environments where requirements of tasks and available resources can be altered throughout task execution. Under the proposed scheme, mobile client devices are able to self-collaborate without a dedicated central server by spontaneously electing a task coordinator among them. The proposed scheme also deals with dynamic events such as the joining and leaving of users, clients and tasks, and supporting dynamic reallocation while keeping them transparent for end-users. Finally, the proposed scheme is evaluated through simulations with different numbers of services and client devices, showing improved results in performance optimality, assignment efficiency, and dependency coverage of composition.

By leveraging location data in online social networks, Location-based Social Networks (LBSNs) can support diverse human activities such as tourism. Different applications aim to aid tourists and provide better experience in their travels by matching co-located users based on what they have in common. However, users with little in common but with potential to help each other given the context and place could not be matched. In this paper we introduce traMSNet, a LBSN that implements a matching algorithm considering homophily, as well as users complementary skills in a touristic location. Our idea is validated with a survey that asked potential travelers about their needs when looking for a travel partner. Moreover, we present a matching algorithm that is evaluated it with real tourists. The evaluation shows that considering complementarity when matching individuals is preferred by users. Therefore, by only considering similarities, important issues are left aside.

The evolution of the Web has allowed the generation of several platforms for collaborative work. One of the main contributors to these advances is the Open Source initiative, in which projects are boosted to a new level of interaction and cooperation that improves their software quality and reliability. In order to understand how the group of contributors interacts with the software under development, we propose a novel methodology that adapts Lotka-Volterra-based biological models used for host-parasite interaction. In that sense, we used the concept mutualism from social parasites. Preliminary results based on experiments on the Github collaborative platform showed that Open Source phenomena can be modeled as a mutualistic system, in terms of the evolution of the population of developers and repositories.

The proactive and spontaneous delivery of Web services for users on the move can lead to the depletion of their cognitive resources, affecting the normal processes of their physical activities. This is due to the competition for limited cognitive resources between the human-computer interactions required by Web services and the users' physical activities. This paper introduces a mechanism for binding and scheduling Web services based on an assessment of this competition for users on the move. The proposed approach is built on two theories from cognitive psychology. This mechanism is realized by a descriptive model of activities and Web services which is enriched with a cognitive layer. A computational model uses this description to assess the degree of the demand for cognitive resources by both the physical activities and the Web services. Additionally, a Web services coordination mechanism based on this level of demand, the principle of progressive disclosure, and the temporal concurrency of Web services ensures less cognitively taxing Web service compositions.

SUMMARY Intelligent service robots provide various services to users by understanding the context and goals of a user task. In order to provide more reliable services, intelligent service robots need to consider various factors, such as their surrounding environments, users' changing needs, and constrained resources. To handle these factors, most of the intelligent service robots are controlled by a task‐based control system, which generates a task plan that represents a sequence of actions, and executes those actions by invoking the corresponding functions. However, the traditional task‐based control systems lack the consideration of resource factors even though intelligent service robots have limited resources (limited computational power, memory space, and network bandwidth). Moreover, system‐specific concerns such as the relationships among functional modules are not considered during the task generation phase. Without considering both the resource conditions and interdependencies among software modules as a whole, it will be difficult to efficiently manage the functionalities that are essential to provide core services to users. In this paper, we propose a mechanism for intelligent service robots to efficiently use their resources on‐demand by separating system‐specific information from task generation. We have defined a sub‐architecture that corresponds to each action of a task plan, and provides a way of using the limited resources by minimizing redundant software components and maintaining essential components for the current action. To support the optimization of resource consumption, we have developed a two‐phase optimization process, which is composed of the topological and temporal optimization steps. We have conducted an experiment with these mechanisms for an infotainment robot, and simulated the optimization process. Results show that our approach contributed to increase the utilization rate by 20% of the robot resources. Copyright © 2011 John Wiley & Sons, Ltd.

The vision of spontaneously delivering ubiquitous services to mobile users stands on the tradition of reflecting functional and non-functional requirements. Nevertheless, existing approaches do not reason in advance about the potential conflicts of mobility activities with human-computer interaction based tasks. These conflicts may burden the user with unexpected interruptions that overload the central human-cognitive capacity. This paper introduces a novel cognitive engineering mechanism to optimize service functionality co ordinations during runtime in accordance with situational demands of cognitive resources. We base our resource aware approach for service coordination optimization on two theories from cognitive psychology - the human-processing system theory of Navon and the multiple resource theory of Wickens. On top of this psychological background, we introduce a specific mechanism for varying service co ordinations.

Overlay networks are opening up new ways for Internet usability, mainly by adding new services that are not available or cannot be implemented in the current Internet. Overlay networks use application level switches of intermediate nodes. However, the designs of overlay network are based on store-and-forward principles maintained by split TCP connections of hop-by-hop approaches and this can increase delay of packet delivery and cause unreliability of packet transmissions due to buffer overflow of the application level switch of each intermediate node, which has a limited buffer space. Also, hop-by-hop approaches for overlay networks cannot support end-to-end semantics. Thus, this study proposes a new Overlay Transport Protocol for overlay networks by considering management of end-to-end connection and congestion and flow control between source and destination nodes. By using a prototype implementation via simulation with MATLAB, We validate our analytical findings and evaluate the performance of proposed protocol which can provide end-to-end reliable data transfers, and also improve throughput in comparison with a network model where Overlay Transport Protocol layer is not available.

In component-based robot software, it is crucial to monitor software faults and deal with them on time before they lead to critical failures. The main causes of software failures include limited resources, component-interoperation mismatches, and internal errors of components. Message-sniffing is one of the popular methods to monitor black-box components and handle these types of faults during runtime. However, this method normally causes some performance problems of the target software system because the fault monitoring and detection process consumes a significant amount of resources of the target system. There are three types of overheads that cause the performance degradation problems: frequent monitoring, transmission of a large amount of monitoring-data, and the processing time for fault analysis. In this paper, we propose an interaction-pattern-based approach to reduce the performance degradation caused by fault monitoring and detection in component-based service robot software. The core idea of this approach is to minimize the number of messages to monitor and analyze in detecting faults. Message exchanges are formalized as interaction patterns which are commonly observed in robot software. In addition, important messages that need to be monitored are identified in each of the interaction patterns. An automatic interaction pattern-identification method is also developed. To prove the effectiveness of our approach, we have conducted a performance simulation. We are also currently applying our approach to silver-care robot systems.

In software development, it is important to mediate various concerns coming from user experience (UX) designers and application developers. In Agile User-Centered Design (Agile-UCD), there is a special role called specialist who is dedicated to implement application features as well as to monitor user experiences. However, the specialist normally has difficulty in linking user tasks to be accessed via a user interface (UI) into application feature entities. In addition, the specialist may also have some unsettled usability risks that might result in the failure of meeting certain usability criteria and passing acceptance tests. To alleviate these difficulties of the specialists in Agile-UCD, we propose a usability-pattern-based requirement-analysis method. This method uses standardized and common representations of requirements specification to bridge the gap between user tasks and related application features of a UI. It also provides a guideline to allow the specialist to reduce usability risks in an early stage by reflecting usability factors of UI design patterns to an application design. A case study has been conducted to show how users can effectively specify user tasks and application features on UI workflows. It also shows how easy and practical it is to understand the common representations as well as to apply to usability patterns.

In this paper, we reflect upon the past five years of the KAIST-Carnegie Mellon MSE (Master of Software Engineering) collaboration, and look ahead to the ways in which we can improve in the years to come. With the understanding that the major component of the program lies in its curriculum, our insights focus mainly in the areas of curriculum improvement and evolution. As a means of achieving this goal, two surveys were conducted, one addressing reflections by the program's participating faculty and graduates, and a second investigating various reference curriculums. Based upon the results of both surveys, an improved curriculum structure is proposed, one that identifies and introduces special track options that the authors propose might better serve the needs and demands of Korean industry.

Abstract Most routing protocols in mobilead hocnetworks (MANETs) place an emphasis on finding paths in dynamic networks without considering security. As a result, there are a number of attacks that can be used to manipulate the routing in MANET. A malicious node that sends a modified control message to an intermediate node can disturb the network using a control message. To solve this problem, we introduce AODV protocol with route investigation procedure (AODV‐RIP). It uses two additional control messages to defeat security attacks that can occur in AODV routing protocol. When an intermediate node that is on the path between the source node and the destination node receives a control message, it sends a Rroute Investigation Request (IREQ) message to the destination node in order to check the reliability of the control message. According to the existence of Route Investigation Reply (IREP), the intermediate node decides whether it transmits the control message to the source node or not. Consequently, the intermediate node that receives the control message confirms that it is using two additive control messages: IREQ and IREP. Through this investigation procedure, the source node can obtain a reliable path for transmitting data packets to an intentional destination node. The simulation results show an improvement in the packet delivery ratio and end‐to‐end delay at the expense of a moderate increase of the control message overhead compared with the current routing protocols. Copyright © 2009 John Wiley & Sons, Ltd.

In order to provide services more reliably, intelligent service robots need to consider various factors, such as their surrounding environments, user's changing requirements, and constrained resources. Most of the intelligent service robots are controlled based on a task-based control system, which generates a task plan that consists of a sequence of actions, and executes the actions by invoking the corresponding functions. However, this task-based control system did not seriously consider resource factors even though intelligent service robots have limited resources (limited computational power, memory space, and network bandwidth). If we consider these factors during the task generation time, the complexity of the plan may become unmanageable. Therefore, in this paper, we propose a mechanism for robots to efficiently use their resources on-demand. We define reusable software-architectures corresponding to each action of a task plan, and provide a way of using the limited resources by minimizing redundant software components. We conducted an experiment of this mechanism for an infotainment robot. The experiment shows the effectiveness of our mechanism.

Self-growing software is a software system that grows its functionalities and configurations by itself based on dynamically monitored situations. Self-growing software is especially necessary for intelligent service robots, which monitor their surrounding environments and provide appropriate behaviors for human users. Intelligent service robots often face problems that cannot be resolved with the conventional software technology. To support self-growing software for intelligent service robots, the SemBots project at ICU is developing a repository framework that allows robot software to dynamically acquire software components that are necessary to resolve a dynamic situation. In this paper, we describe the requirements and architecture of the repository system for self-growing software. We also present a prototype implementation of the repository system.

Major characteristics of sensor networks, such as node mobility, collaboration among heterogeneous sensors, and environmental changes, require flexible and adaptive software systems. It is also essential to have a mechanism to describe configurations of distributed components and to coordinate the sensors in the network through the configuration information. In this paper, we show how an architectural model can be used to support adaptive software systems for sensor networks. This work is based on our previous work of using an architecture description language (ADL) to represent configurations of software components in sensor nodes and to reconfigure them in response to changing needs of users. We use XML as a means to exchange and manipulate ADL descriptions. To demonstrate how the architecture-based adaptation works for sensor networks, we simulated a scenario of reconfiguring a sensor network to handle node failures.

Dynamic Web content provides us with time-sensitive and continuously changing data. To glean up-to-date information, users need to regularly browse, collect and analyze this Web content. Without proper tool support this information management task is tedious, time-consuming and error prone, especially when the quantity of the dynamic Web content is large, when many information management services are needed to analyze it, and when underlying services/network are not completely reliable. This paper describes a multi-level, lifecycle (design-time and run-time) coordination mechanism that enables rapid, efficient development and execution of information management applications that are especially useful for processing dynamic Web content. Such a coordination mechanism brings dynamism to coordinating independent, distributed information management services. Dynamic parallelism spawns/merges multiple execution service branches based on available data, and dynamic run-time reconfiguration coordinates service execution to overcome faulty services and bottlenecks. These features enable information management applications to be more efficient in handling content and format changes in Web resources, and enable the applications to be evolved and adapted to process dynamic Web content.