Social media platforms such as Twitter, Reddit, and Sina Weibo playa crucial role in global communication but often encounter strict regulations in geopolitically sensitive regions. This situation has prompted users to ingeniously modify their way of communicating, frequently resorting to coded language in these regulated social media environments. This shift in communication is not merely a strategy to counteract regulation, but a vivid manifestation of language evolution, demonstrating how language naturally evolves under societal and technological pressures. Studying the evolution of language in regulated social media contexts is of significant importance for ensuring freedom of speech, optimizing content moderation, and advancing linguistic research. This paper proposes a multi-agent simulation frame-work using Large Language Models (LLMs) to explore the evolution of user language in regulated social media environments. The framework employs LLM-driven agents: supervisory agent who enforce dialogue supervision and participant agents who evolve their language strategies while engaging in conversation, simulating the evolution of communication styles under strict regulations aimed at evading social media regulation. The study evaluates the framework's effectiveness through a range of scenarios from abstract scenarios to real-world situations. Key findings indicate that LLMs are capable of simulating nuanced language dynamics and interactions in constrained settings, showing improvement in both evading supervision and information accuracy as evolution progresses. Furthermore, it was found that LLM agents adopt different strategies for different scenarios. The reproduction kit can be accessed at https://github.com/BlueLinkXlGA-MAS.

Correct behavior of flight software against its requirements is a prime concern spanning its design, implementation, and operation. The emergence of “New Space” presents new challenges associated with small-scale missions which often involve open software frameworks, are developed by diverse teams, and employ rapid development methodologies which may not enjoy the rigorous quality assurance that institutional missions do. Hence, there is an overarching need to incor-porate contemporary engineering techniques and methods for checking requirement satisfaction for such flight software. To this end, this paper proposes GOP RIM E, designed to integrate goal monitoring within F’, a renowned software development framework developed by the Jet Propulsion Laboratory for embedded and spaceflight systems. GOPRIME consists of three phases: (i) a design phase, where annotations are used to align system-level objectives with architectural components; (ii) an implementation phase, where code generation tailored for the DSL of F’ is used to seamlessly automate the integration of goal monitoring functionality, and (iii) the operational phase, where the runtime state of annotated components is monitored, enabling evaluation of satisfaction of the overall goal model. We assess the development and operation overheads of instrumenting runtime goal monitoring over a characteristic case of a miniaturized satellite application.

Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a feedback loop with four core functionalities: monitoring, analyzing, planning, and execution. Recently, generative artificial intelligence (GenAI), especially the area of large language models, has shown impressive performance in data comprehension and logical reasoning. These capabilities are highly aligned with the functionalities required in SASs, suggesting a strong potential to employ GenAI to enhance SASs. However, the specific benefits and challenges of employing GenAI in SASs remain unclear. Yet, providing a comprehensive understanding of these benefits and challenges is complex due to several reasons: limited publications in the SAS field, the technological and application diversity within SASs, and the rapid evolution of GenAI technologies. To that end, this article aims to provide researchers and practitioners a comprehensive snapshot that outlines the potential benefits and challenges of employing GenAI’s within SAS. Specifically, we gather, filter, and analyze literature from four distinct research fields and organize them into two main categories to potential benefits: (i) enhancements to the autonomy of SASs centered around the specific functions of the MAPE-K feedback loop, and (ii) improvements in the interaction between humans and SASs within human-on-the-loop settings. From our study, we outline a research roadmap that highlights the challenges of integrating GenAI into SASs. The roadmap starts with outlining key research challenges that need to be tackled to exploit the potential for applying GenAI in the field of SAS. The roadmap concludes with a practical reflection, elaborating on current shortcomings of GenAI and proposing possible mitigation strategies. †

Decision making in self-adaptive systems often involves trade-offs between multiple quality attributes, with user preferences that indicate the relative importance and priorities among the attributes. However, eliciting such preferences accurately from users is a difficult task, as they may find it challenging to specify their preference in a precise, mathematical form. Instead, they may have an easier time expressing their displeasure when the system does not exhibit behaviors that satisfy their internal preferences. Furthermore, the user’s preference may change over time depending on the environmental context; thus, the system may be required to continuously adapt its behavior to satisfy this change in preference. However, existing self-adaptive frameworks do not explicitly consider dynamic human preference as one of the sources of uncertainty. In this paper, we propose a new adaptation framework that is specifically designed to support self-adaptation to user preference. Our framework takes a human-on-the-loop approach where the user is given an ability to intervene and indicate dissatisfaction and corrections with the current behavior of the system; in such a scenario, the system automatically updates the existing preference values so that the new, resulting behavior of the system is consistent with the user’s notion of satisfactory behavior. To perform this adaptation, we propose a novel similarity analysis to produce changes in the preference that are optimal with respect to the system utility. We illustrate our approach in a case study involving a delivery robot system. Our preliminary results indicate that our approach can effectively adapt its behavior to changing human preference.

3D object detection plays a vital role in the perception system of self-driving cars for it provides accurate structural information and classification of objects in the scene. Recent works leverage pseudo point clouds to compensate for the sparsity of raw point clouds. However, the coarse pseudo point cloud generation brings huge computational costs to the inference process, resulting in inferior inference speed. In this work, we aim to solve two critical yet not well-addressed issues in pseudo point cloud generation, including the loss of 3D detection inference speed and the additional memory cost due to the massive generation of the pseudo point cloud. We propose attention-guided pseudo point cloud generation to direct the focus of pseudo point cloud generation to hazardous regions. In our experiments, our method improves the inference speed by 21.72% and reduces the memory usage of generated data by 90.03%.

Reflection is a technique that enables a system to inspect or change its structure and/or behavior at runtime. It is a key enabler of many techniques for developing systems that have to function despite rapidly changing requirements and environments. A crucial issue in developing reflective systems is to ensure the correctness of their behaviors, because object-level behaviors are affected by metalevel behaviors. In this paper, we present an extended labeled transition system (LTS), which we call a metalevel LTS (MLTS), that supports data representation of another LTS for use in modeling a reflective tower. We show that two of the existing state reduction techniques for an LTS (symmetry reductionanddivergence-sensitive stutter bisimulation) are also applicable to an MLTS. Then, we introduce two strategies for implementing an MLTS model in Promela, thereby enabling verification with the SPIN model checker. We also present case studies of applying MLTSs to two reflection applications: self-adaptation of a reconnaissance robot system, and dynamic evolution of an Internet-of-things (IoT) system. The case studies demonstrate the applicability of our approach and its scalability improvement through the state reduction techniques.

Reinforcement learning is a powerful methodology that enables self-adaptive systems to relearn and update their adaptation policy when dealing with unforeseen changes. To update the policy more efficiently, several knowledge reuse approaches have been proposed to speed up relearning. However, the current studies treat and reuse the knowledge integrally, which may result in increased relearning costs if the reused knowledge is inappropriate in the changed situation. Generally, some localized pieces of the knowledge are still appropriate for reuse if they are not related to the changes, while some pieces may become inappropriate for reuse if they are affected by the changes. This paper proposes a goal-oriented curriculum evolution method to realize finer-grained knowledge reuse, combining goal-oriented modeling and curriculum learning. The method is twofold: (1) at design time, we apply goal-oriented modeling to design a curriculum in which an RL problem is decomposed into sub-problems, so that knowledge can be decomposed into several pieces of localized knowledge for sub-problems, and (2) at runtime, we evolve the curriculum to reflect changes (i.e., update the sub-problems related to the changes), so that the affected pieces of knowledge can be locally updated to make them appropriate for reuse in the changed situation. The evaluation based on a cleaning robot shows that the relearning time was shortened, demonstrating the effectiveness of our method.

In the studies of self-adaptive systems (SAS), requirement relaxation is a widely discussed approach for managing the system’s requirements when dealing with the runtime environment changes (e.g., ignoring low-priority requirements to guarantee high-priority requirements). Guaranteeable requirement analysis (GRA) is recently proposed to determine the relaxation by checking the feasibility of all requirement combinations, enabling the SAS to realize the relaxation autonomously. However, a critical problem of GRA is the trade-off between analysis/relaxation precision and computation time at different granularity levels of requirements. Specifically, the analysis may not be precise enough if the requirements are coarse-grained (i.e., high granularity level), while the analysis may take a too long time if the requirements are fine-grained (i.e., low granularity level). This paper proposed a method, namely iterative adaptation via multi-grained requirement relaxation, to achieve the advantages of high precision and short computation time. Specifically, the SAS first deploys a rapid (but imprecise) relaxation using high granularity-level requirements. It then repeatedly iterates to a preciser (but slower) relaxation with a progressive decrease in the granularity level. An experiment based on the warehouse robot system demonstrates the validity of our proposal.

The Smart Systems and Services Innovative Professional Education (Smart SE) program is a certification program developed as part of the education network for the Practical information Technologies (enPiT-Pro) project, which is funded by the Japan Ministry of Education, Culture, Sports, Science and Technology. The Smart SE program provides industry professionals working in fields related to information and communication technology (ICT) with additional training and education in smart systems and services that utilize various technologies such as IoT, Cloud, Big Data, and Artificial Intelligence (AI) for businesses. Here, we illustrate its purpose, curriculum and features to respond to the needs of industrial professional education.

In event-based systems, safety properties are critical requirements to prevent the system from bad things happen. However, safety properties may be violated because of the runtime system functional fault. From the viewpoint of a self-adaptive system, such a system should be requirement-aware and changes its behavior to satisfy the designed requirements as much as possible. The previous work proposed a method to analyze possible adaptation options with degrading different requirements. Here, we propose an efficient difference analysis algorithm to shorten the analysis time so that the adaptation to functional fault can be more timely. Our idea is to reuse the analysis result of development time and re-analyze the changed part only, instead of performing the complete analysis from scratch. We evaluated our algorithm's efficiency based on three case studies: a coalmine pump-control system, a cyber-physical security people-flow restriction system, and a factory production cell system. The experiment results indicate that our algorithm averagely reduces 75.9% of analysis time compared with the existing analysis technique.

Discrete event controllers are at the heart of many software systems that require continuous operation. Changing these controllers at runtime to cope with changes in its execution environment or system requirements change is a challenging open problem. In this paper we address the problem of dynamic update of controllers in reactive systems. We present a general approach to specifying correctness criteria for dynamic update and a technique for automatically computing a controller that handles the transition from the old to the new specification, assuring that the system will reach a state in which such a transition can correctly occur and in which the underlying system architecture can reconfigure. Our solution uses discrete event controller synthesis to automatically build a controller that guarantees both progress towards update and safe update.

The recent boom in social networking services has prompted the research of recommendation systems. The basic assumption behind these works was that "users’ preference is similar to or influenced by their friends". Although many studies have attempted to use social relations to enhance recommendation system, they neglected that the heterogeneous nature of online social networks and the variations in users' trust of friends according to different items. As a natural symmetry between the latent preference vector of the user and her friends, we propose a new social recommendation method called ScAN (short for “co-Attention Neural Network for Social Recommendation”). ScAN is based on a co-attention neural network, which learns the influence value between the user and her friends from the historical data of the interaction between the user/her friends and an item. When the user interacts with different items, different attention weights are assigned to the user and her friends respectively, and the user's new latent preference feature is obtained through an aggregation strategy. To enhance the recommendation performance, a network embedding technique is utilized as a pre-training strategy to extract the users’ embedding and to incorporate the extracted factors into the neural network model. By conducting extensive experiments on three different real-world datasets, we demonstrate that our proposed method ScAN achieves a superior performance for all datasets compare with state-of-the-art baseline methods in social recommendation task.

To meet future demands for wireless sensor network (WSN) software, both experts and average software developers should be involved in WSN software development. However, WSN software development is difficult for the average software developer because data processing-related design and network-related design are tangled in the software. Here, we propose a software development process for WSN software by stepwise refinement. Our process enables stepwise refinement to separately address data processing-related and network-related concerns, reuse of well-defined designs, and implementations for network-related concerns prepared by the experts, and perform model-driven development to obtain source codes from models by model transformations. Additionally, we used case studies using actual WSN software development and user studies to evaluate how our proposed process can support actual WSN software development.

Wireless sensor networks (WSNs) play important roles in systems that interact with the real world (e.g., cyber-physical systems and internet of things). To mitigate the complex nature of WSNs, many platforms at different abstraction levels (e.g., abstraction of hardware, communications, and individual nodes) have been proposed in the last decade. WSNs in different environments might employ different platforms to satisfy nonfunctional requirements (NFRs) because the diversity of platforms results in trade-offs of NFRs and the quality of WSN heavily depends on the environment it runs. Although WSN software should be highly portable, existing approaches only support porting between operating systems and not more abstract middleware. Herein we propose a methodology with a framework to capture various platforms in the WSN domain with platform-independent multi-level models. A case study demonstrates that our framework can port WSN software to platforms at different abstraction levels and adapt the software to the new platform to improve performance.

In systems that require several services to collaborate, specifying coordination protocols is vital, but costly. Additionally, several properties, which are derived from laws, regulations, requirements, etc., must be satisfied. Coordination protocol composition approaches construct specific protocols in a cost effective manner in accordance with the composition intentions. However, existing composition approaches are insufficient in terms of satisfying properties. Existing approaches use concrete specifications to identify composition intentions and do not consider interference between compositions. Herein we propose a new composition approach in which a developer directly expresses his or her intentions as constraints via metadata, and then the system searches for optimal composition methods based on the constraints.

Wireless Sensor Network (WSN) deployment experiences show that data collected is prone to be imprecise and faulty due to internal and external influences, such as battery drain, environmental interference, sensor aging. An early detection of such faults is necessary for the effective operation of the sensor network. In this preliminary work, we propose a hybrid approach to the detection of faults and we illustrate its performance on data coming from a real sensor deployment. The proposal is a first step to have a hybrid method towards automated on-line fault detection and classification in context-aware WSNs middleware framework.

In e-business, agents need to coordinate with each other. Coordination protocols that specify the defining orders of message passing are very important. The scale of e-Business grows with the advancement of technology, and the number of agents involved continues to increase. Specifying the coordination protocols for so many participating agents is a complex task. A coordination protocol composition approach reduces the complexity of specifying such a coordination protocol. It treats coordination protocols as individual parts and composes them to construct the intended protocols. However, existing approaches do not sufficiently reduce the complexity when a coordination protocol is composed several times, because too many configurations are required to specify a composition. A new approach is proposed that uses metadata to specify the compositions by specifying only one configuration. It can reduce the number of configurations when a coordination protocol is composed several times.

Multi-agent systems need protocols to coordinate among agents implemented by different owners. However, specifying coordination protocols for many participating agents is a complex task. A protocol composition approach, which can reduce the complexity of specifying such a coordination protocol, must specify how to compose coordination protocols in a composition configuration. Current protocol compositions cannot sufficiently reduce the complexity because composing a protocol several times requires the specification of too many configurations. We propose a protocol composition approach that can specify composition configurations in an abstract way. We assign metadata to the messages in a coordination protocol and use the metadata to specify the configuration. An abstract configuration using metadata can be applied to various protocol compositions and can reduce the number of specifications required for configuration composition.

Middleware for wireless sensor network (WSN) abstracts a network as an entity and hides programming difficulties from programmers. Many middlewares have been proposed, but they use different programming languages to manipulate functions in WSNs. This inhibits usability when manipulating multiple WSNs managed by different middlewares, because the primitives of each language have different descriptive capabilities. In this paper, we propose and apply design patterns in WSN programming to complement the capabilities of language primitives, and discuss the effectiveness of these design patterns. First, we discuss major middleware languages and compare the capabilities of their primitives. Second, we extract design patterns from the representative middlewares to cover the missing capabilities identified in the comparison. Finally, we discuss the effectiveness of design patterns for WSN programming. The discussion indicates that design patterns improve the usability of manipulating multiple WSNs.

A location-specific data retrieval, which is data retrieval from nodes in a designated region at the time, is an attractive application in a mobile ad-hoc network (MANET). However, almost all nodes in a MANET are powered by batteries, the location-specific data retrieval should involve a small number of messages. In this paper, we use a mobile agent to retrieve the location-specific data. A mobile agent migrates to a node in a designated region, and retrieves data from nodes in this region. Since, after migration, the agent can communicate with nodes in the designated region through low overhead short length hops, the mobile agent can retrieve data at low message cost for long periods, even if the owner of this agent moves around. However, even after migrating to node in the designated region, in order to stay near this region, a mobile agent should migrate to other nodes in response to the movement of the node hosting this agent. In this paper, we propose the geographically bound mobile agent (GBMA) which is a mobile agent that periodically migrates in order to always be located in a designated region. In order to clarify where the GBMA should be located and when the GBMA starts to migrate, two geographic zones are set to the GBMA: required zone and expected zone. The required zone ease tracking of the GBMA, and the expected zone ease adjustment of the GBMA migration timing.