People often rely on heuristic reasoning when receiving algorithm advice, and this reliance leads to biased decisions that undermine the effectiveness of human-AI collaboration. Such bias persists even when individuals are given more time to deliberate or provided more information about AI, as they may lack the awareness or ability to engage in systematic reasoning. In this paper, we explore how guided reflection may enhance decision-making performance in human-AI collaboration by prompting a systematic reasoning process. We conducted an experiment with 178 participants, comparing decision-making behavior across three conditions: AI, explainable AI (XAI), and XAI with reflection. The results demonstrate that reflection significantly reduced over-reliance on AI and improved decision accuracy. Individuals with a high need for cognition and a high perceived understanding of AI benefited more from reflection. Furthermore, our study uncovers distinct patterns of cognitive processing and belief adjustment across different experimental conditions. Our findings provide a practical strategy for fostering cognitive engagement and contribute to a deeper understanding of human cognitive processes in AI-assisted decision-making.

Event-centric retrieval requires ranking candidates based on whether they jointly satisfy multiple semantic conditions, rather than isolated pairwise correlations. Such conditions are commonly expressed as symbolic dependencies, including logical, temporal, spatial, and hierarchical constraints. Recent knowledge-driven retrieval models improve ranking by propagating relational evidence over graphs, while hypergraph-based approaches further capture observed n-ary co-occurrence patterns among entities. However, existing methods typically treat symbolic constraints as external filters, post-hoc consistency checks, or training-time regularizers, and therefore fail to execute and propagate them during retrieval. As a result, neither graph-based nor hypergraph-based models support retrieval-time execution of symbolic constraints as first-class ranking evidence, limiting their effectiveness for event-centric queries governed by joint semantic conditions. To address this limitation, we propose CARE, a Constraint-Aware Retrieval framework that integrates symbolic constraints directly into the retrieval process. CARE introduces a lightweight Generative Rule Grammar (GRG) to normalize heterogeneous constraint forms and deterministically compile them into executable Source–Target (S–T) hyperedges, which explicitly encode directional evidence flow from constraint-induced sources to ranking targets. Retrieval is performed via a dual-channel propagation architecture that interleaves relation-aware message passing over observed facts with constraint-aware propagation over S–T hyperedges, enabling unified and interpretable aggregation of relational and symbolic evidence. We evaluate CARE on three mixed-arity benchmarks (JF17K, WikiPeople, and FB-AUTO), where it consistently outperforms strong hypergraph-based ranking baselines and yields transparent, constraint-grounded retrieval rationales in an emergency knowledge graph case study.

On behalf of the technical program committee, we are delighted to invite you to participate in the 2025 IEEE 16th International Conference on Cloud Computing Technology and Science (IEEE CloudCom), to be held in Shenzhen, China on November 14-16, 2025.

Recent advances in Artificial Intelligence (AI) have introduced a popular paradigm in Machine Learning (ML) model development: pre-training and domain adaptation. As both closed-source developers and open-source community lead in pre-training foundation models, domain deployers face the dilemma about whether to use closed-source models via API access or to host open-source models on proprietary hardware. Using closed-source models incurs recurring costs, while hosting open-source models requires substantial hardware investments and may lead to potentially lagging advancements. This paper presents a game-theoretical model to examine the economic incentives behind the deployment choice and the impact of open-source engagement strategies on technological innovation. We find that deployers consistently opt for closed-source APIs when the open-source community engages reactively by maintaining a fixed performance ratio relative to closed-source advancements. However, open-source models can become preferable when a proactive open-source community produces high-performance models independently. Furthermore, we identify conditions under which the engagement and competitiveness of the open-source community can either foster or inhibit technological progress. These insights offer valuable implications for market regulation and the future of technology innovation.

Massive Open Online Courses (MOOCs) have opened new educational possibilities for learners around the world. Most of the research and spotlight has been concentrated on a handful of global, English-language providers, but there are a growing number of regional providers of MOOCS in languages other than English. In this work, we have partnered with thirteen MOOC providers from around the world. We apply a multi-platform approach generating a joint and comparable analysis with data from millions of learners. This allows us to examine learning analytics trends at a macro level across various MOOC providers, with a goal of understanding which MOOC trends are globally universal and which of them are context-dependent. The analysis reports preliminary results on the differences and similarities of trends based on the country of origin, level of education, gender and age of their learners across global and regional MOOC providers. This study exemplifies the potential of macro learning analytics in MOOCs to understand the ecosystem and inform the whole community, while calling for more large scale studies in learning analytics through partnerships among researchers and institutions.

Sharing economy refers to peer-based activities of obtaining, giving, or sharing the access to goods and services, coordinated through community-based online services. It is known as collaborative consumption that people share the services rather than having individual ownership. By leveraging idle resources to produce more goods and services, sharing economy significantly drives green consumption and sustainable development in our human society. Using information technology to provide individuals with information enables the optimization of resources through the mutualization of excess capacity in goods and services. A common premise is that when information is shared, the value of the goods may increase for businesses, for individuals, for communities, and for the whole society in general. Currently, sharing economy has potentially resulted in a great impact on citizens’ everyday life and generated huge economic benefits, e.g., Airbnb, Uber, and Amazon Mechanical Turk. A host of enabling technologies has reached the mainstream for the rise of sharing economy, including open data, the ubiquity of low-cost mobile phones, and social media. These technologies dramatically reduce the friction of share-based business and organizational models.

In this paper, we present a mobility cloud platform called CarCloud that has been designed to facilitate the real-world deployment of ubiquitous mobile telematic applications to promote smart transportation. CarCloud leverages the mobility, sensing and communication capacities of mobile devices, and collaborates with sensors embedded in vehicles (e.g., accessed via OBD-II) and cloud services to form a seamless platform. This platform orchestrates different mobility data in transportations to REST web service based mobility services that could be used for different mobile telematic applications (e.g., enabling traffic managers to monitor the behaviors of drivers) in intelligent transportation systems (ITS). Prototype implementation and preliminary experiments demonstrate the desired functionality of CarCloud for ITS and its feasibility for real-world deployment.

Driving is an integral part of our everyday lives, but it is also a time when people are uniquely vulnerable. Previous research has demonstrated that not only does listening to suitable music while driving not impair driving performance, but it could lead to an improved mood and a more relaxed body state, which could improve driving performance and promote safe driving significantly. In this article, we propose SAfeDJ, a smartphone-based situation-aware music recommendation system, which is designed to turn driving into a safe and enjoyable experience. SAfeDJ aims at helping drivers to diminish fatigue and negative emotion. Its design is based on novel interactive methods, which enable in-car smartphones to orchestrate multiple sources of sensing data and the drivers' social context, in collaboration with cloud computing to form a seamless crowdsensing solution. This solution enables different smartphones to collaboratively recommend preferable music to drivers according to each driver's specific situations in an automated and intelligent manner. Practical experiments of SAfeDJ have proved its effectiveness in music-mood analysis, and mood-fatigue detections of drivers with reasonable computation and communication overheads on smartphones. Also, our user studies have demonstrated that SAfeDJ helps to decrease fatigue degree and negative mood degree of drivers by 49.09% and 36.35%, respectively, compared to traditional smartphone-based music player under similar driving situations.

Driving is an integral part of our everyday lives, but it is also a time when people are uniquely vulnerable. Poor road condition, traffic congestion and long driving time may bring negative emotion to drivers and increase the chance of traffic accidents. We propose SAfeDJ, a situation-aware in-car music delivery application, which turns people's trips into pleasant journeys and driving into a safe and enjoyable activity. SAfeDJ aims at helping drivers to diminish fatigue and negative emotion. It is built on a vehicular healthcare platform that enables communications among drivers and integrates with multiple types of sensors to promote safe driving. Prototype implementation and initial results of SAfeDJ have demonstrated its desired functionality in drivers' daily lives and feasibility for real-world deployment.

We present a comprehensive classification of data misinterpretation problems and develop an approach to automatic detection and reconciliation of data interpretation conflicts in Web services composition. The approach uses a lightweight ontology augmented with modifiers, contexts, and atomic conversions between the contexts. The WSDL descriptions of Web services are annotated to establish correspondences to the ontology. Given the naive Business Process Execution Language (BPEL) specification of the desired Web services composition with data interpretation conflicts, the approach can automatically detect the conflicts and produce the corresponding mediated BPEL. Finally, we develop a prototype to validate and evaluate the approach.

Recently, mediation-aided composition has been widely adopted when dealing with incompatibilities of services. However, existing approaches suffer from state space explosion in compatibility verification and cannot automatically generate the BPEL code. This paper presents a Petri net approach to mediation-aided composition of Web services. First, services are modeled as open WorkFlow Nets (oWFNs) and are composed using mediation transitions (MTs). Second, the modular reachability graph (MRG) of composition is automatically constructed and the compatibility is analyzed, so that the problem of state space explosion is significantly alleviated. Furthermore, an Event-Condition-Action (ECA) rule-based technique is developed to automatically generate the BPEL code of the composition, which can significantly save the time and labor of designers. Finally, the prototype system has been developed.

Current methods that deal with concurrent workflow temporal violations only focus on checking whether there are any temporal violations. They are not able to point out the path where the temporal violation happens and thus cannot provide specific solutions. This paper presents an approach based on a sprouting graph to find out the temporal violation paths in concurrent workflow processes as well as possible solutions to resolve the temporal violations. First, we model concurrent workflow processes with time workflow net and a sprouting graph. Second, we update the sprouting graph at the checking point. Finally, we find out the temporal violation paths and provide solutions. We apply the approach in a real business scenario to illustrate its advantages: 1) It can dynamically check temporal constraints of multiple concurrent workflow processes with resource constraints; 2) it can give the path information in the workflow processes where the temporal violation happens; and 3) it can provide solution to the temporal violation based on the analysis.

Web services have become the technology of choice for service-oriented computing implementation, where Web services can be composed in response to some users' needs. It is critical to verify the compatibility of component Web services to ensure the correctness of the whole composition in which these components participate. Traditionally, two conditions need to be satisfied during the verification of compatibility: reachable termination and proper termination. Unfortunately, it is complex and time consuming to verify those two conditions. To reduce the complexity of this verification, we model Web services using colored Petri nets (PNs) so that a specific property of their structures is looked into, namely, well structuredness. We prove that only reachable termination needs to be satisfied when verifying behavioral compatibility among well-structured Web services. When a composition is declared as valid and in the case where one of its component Web services fails at run time, an alternative one with similar behavior needs to come into play as a substitute. Thus, it is important to develop effective approaches that permit one to analyze the similarity of Web services. Although many existing approaches utilize PNs to analyze behavioral compatibility, few of them explore further appropriate definitions of behavioral similarity and provide a user-friendly tool with automatic verification. In this paper, we introduce a formal definition of context-independent similarity and show that a Web service can be substituted by an alternative peer of similar behavior without intervening other Web services in the composition. Therefore, the cost of verifying service substitutability is largely reduced. We also provide an algorithm for the verification and implement it in a tool. Using the tool, the verification of behavioral similarity of Web services can be performed in an automatic way.

Examining interactions between Web services is of paramount importance to the success of service composition. We have previously proposed a 2-layer framework for modeling, analyzing, and managing these interactions. Interactions are assigned to two layers: business logic and support. The business-logic layer comprises control and transactional flows, whereas the support layer comprises exception and message flows. This paper continues this research effort by focusing on the synchronization of the four flows at run-time. In particular, we discuss the synchronization mechanisms integrated into the 2-layer framework and report our preliminary experiments on the implementation.

The potential benefits of Web services composition heavily rely on semantic interoperability, i.e., the ability to exchange data meaningfully amongst Web services. Context heterogeneity, which refers to different implicit assumptions about interpreting the exchanged data, hampers the automatic composition of Web services. However, existing initiatives of semantic Web services (SWSs) often ignore context heterogeneity. In this paper, we introduce an approach to address this issue. The contexts of the involved Web services are defined in a lightweight ontology and their WSDL descriptions are annotated by an extension of a W3C standard, i.e., semantic annotation for WSDL and XML schema (SAWSDL). The composition of Web services is described using BPEL specification. Given a BPEL file that ignores context heterogeneity, the approach automatically detects all context differences among the involved services, and reconciles them by producing a mediated BPEL file that incorporates necessary conversions using Xpath functions and/or Web services.

Service composition is one of the key objectives for adopting service oriented architecture. Today, Web services, however, are not always perfectly compatible and composition mismatches are common problems. Service mediation, generally classified into signature and protocol ones, thus becomes one key working area in SOA. While the former has received considerable attention, protocol mediation is still open and current approaches provide only partial solutions. In this paper, a pattern-based approach is proposed for developers to semi-automatically generate mediators and glue partially compatible services together. Based on the investigation on workflow patterns and message exchanging sequences in service interactions, several basic mediator patterns are developed and can be used to modularly construct advanced mediators that can resolve all possible protocol mismatches, especially such mismatches about complicated control logics. Moreover, the architecture for the service mediation system is designed and implemented to prove the feasibility of our approach.