In order to address challenges related to the absence of visual modality and coarse-grained semantic inconsistency in multi-modal entity alignment, we present a novel framework named Context-based Image Generation and Fine-Grained Semantic Structure Embedding (CIGFSE). First, CIGFSE leverages the textual context of entities along with large language models to generate prompts for entities lacking images, which are then input into image generation models to produce auxiliary images enriching the multi-modal knowledge graph. Next, it captures structural information via semantic-augmented structure embedding and applies a feedforward neural network to the other modalities. Furthermore, CIGFSE adopts attention-guided modality fusion and contrastive learning to optimize the model. Extensive experiments on both monolingual and bilingual datasets demonstrate that CIGFSE achieves state-of-the-art performance in multi-modal entity alignment.

In order to ensure that the web application can continue to provide high-quality services after implementing the bandwidth management schemes, the administrators usually need to predict the Quality of Service(QoS) in advance according to the hypothetical changes of the web system. In the existing research, few pay attention to the prediction of QoS in bandwidth-driven evolutionary scenarios. In this paper, we propose a solution comprised of automated data mining skills and transfer learning techniques to predict the response time of web services in bandwidth-driven evolutionary scenarios. We choose a suitable approach of domain adaptation according to the characteristics of the QoS prediction problems in this paper. There are mainly three contributions in this paper: 1) we adopt an automated data mining approach to extract features for prediction. 2) To our knowledge, it is the first time to apply domain adaptation to the QoS prediction of web services in evolutionary scenarios. 3) We perform some experiments to evaluate the effectiveness and stability of the proposed solution, including two types of evolutionary scenarios under a realworld web application.

Minimizing network usage is often a key driver of cost control for web services in the cloud. Most of the literature focuses on resources such as CPU, memory, and disk but the bandwidth is somehow neglected. In fact, it's very challenging to predict the network throughput of modern web services due to the factors of miscellaneous service responses and complex network transportation. Previously we proposed a what-if analysis approach named Log2Sim to predict bandwidth consumption and response time of web services based on network simulation, however, how to use it to address bandwidth problems in different changing contexts is not well studied. In this paper, we work this out with some extensions of Log2Sim, and a general process of bandwidth planning in different changing contexts is given. We also use a classic web benchmark and design various controlled experiments to evaluate this extended approach from different perspectives in multiple scenarios.

For resource management purpose, administrators usually need to perform what-if analyses to predict the impact of any workload growths or planned changes on the performance of web services. A what-if analysis requires not only the design of system models, but also the workload models that represent the real-world user behavior. Existing methods of workload characterization based on probabilistic graphical models are quite complex if there are many web services provided by a system. Meanwhile, bandwidth resource is usually not taken into account in many related works, though it is a relatively expensive resource in cloud markets. In fact, it's very challenging to predict the network throughput of modern web services due to the factors of client-side caching, miscellaneous service responses and complex network transportation. In this paper we propose a methodology of what-if analysis named Log2Sim for the bandwidth management of web systems. We use a lightweight workload model to describe user behavior, an automated mining approach to obtain characteristics of workloads and responses from massive web logs, and traffic-aware simulations to predict the impact on the network throughput and the response time within changing contexts of user behavior. We also choose a real-life web system as use case to evaluate the effectiveness, accuracy and stability of this methodology.

To maintain a complex modern web-based system, the what-if analysis is quite useful for predicting the impact of any workload growth or planned change on the performance of individual components, even the entire system. In this paper we propose an approach based on log mining to build what-if models of web-based systems and validate them by using OPNET simulation tool. Three contributions of this paper are: (1) the construction of what-if models based on log mining to automate the task of modeling and validation, (2) the technique to address complexity of user behaviors including users' browser caching, clustering of requests and users, etc., (3) and two real-life cases to evaluate the effectiveness and accuracy of this approach. The preliminary results show that the relative errors between logged data and simulation results are less than 18% in most cases including number of requests and network throughput.

Quality evaluation is a critical aspect in the area of software development.If software quality problems could be found in the early design phase, the cost for software development and maintaining will be reduced.In this paper we propose an evaluation framework including a software error model and its corresponding experimental frame, which is based on Discrete Event System Specification (DEVS), to support the evaluation of multiple quality properties in the design phase.To accelerate the modeling and simulation processes, we further extend E-DEVSML to create model of system under evaluation and its experimental frame, and transform them to executable models automatically.A case study of a ticket booking system is presented to demonstrate that our approach is applicable.

The activity diagram is widely used for describing and understanding workflows, however, it lacks a formal semantics and cannot be manipulated by computer. In this paper, we present a transformation from UML activity diagrams with time properties to timed coloured petri nets (TCPNs) in a formal way. We extend the activity diagram with time properties and a formal model named extended activity hyper graph (EAH) is defined. By mapping this formal model to a clocked transition system (CTS), we define a weak semantics for it. A list of transformation rules are proposed to show how this can be transformed to a TCPN model. We partially prove the model equivalence of the two models. Finally, an example of Game Capture application is used to validate our approach.