As large language models (LLMs) generate text that increasingly resembles human writing, the subtle cues that distinguish AI-generated content from human-written content become increasingly challenging to capture. Reliance on generator-specific artifacts is inherently unstable, since new models emerge rapidly and reduce the robustness of such shortcuts. This generalizes unseen generators as a central and challenging problem for AI-text detection. To tackle this challenge, we propose a progressively structured framework that disentangles AI-detection semantics from generator-aware artifacts. This is achieved through a compact latent encoding that encourages semantic minimality, followed by perturbation-based regularization to reduce residual entanglement, and finally a discriminative adaptation stage that aligns representations with task objectives. Experiments on MAGE benchmark, covering 20 representative LLMs across 7 categories, demonstrate consistent improvements over state-of-the-art methods, achieving up to 24.2% accuracy gain and 26.2% F_1 improvement. Notably, performance continues to improve as the diversity of training generators increases, confirming strong scalability and generalization in open-set scenarios. Our source code will be publicly available at https://github.com/PuXiao06/DRGD.

Speech-driven 3D facial animation has been a long-standing topic due to the complex geometry and motion modeling as well as difficulties in cross-modality learning. Current studies struggle to synthesize human-like lip motions, as they usually represent the movement of the entire face with a compressed global vector, leading to subtle motion loss and thus over-smoothed movements in the local lip region. To cope with this problem, we propose a new speech-driven 3D facial animation framework RQTalker based on the Region-aware Vector Quantization mechanism. The key insight is to first build a region-aware codebook via a self-reconstruction manner, in which each part of the codebook physically corresponds to a facial region with a clear semantic. Our region-aware codebook divides facial movements into local regions for multiple sub-encodings, reducing information loss from compression and improving local facial motion modeling. In addition, we further propose a spatial-temporal Audio-to-Motion Learning Module to produce movements that are spatially more accurate and temporally consistent. Qualitative and quantitative results demonstrate that our method outperforms state-of-the-art approaches.

Abstract Detecting fake news early is challenging due to the absence of labeled articles for emerging events in training data. To address this, we propose a Disentangled Event-Agnostic Representation (DEAR) learning approach. Our method begins with a BERT-based adaptive multi-grained semantic encoder that captures hierarchical and comprehensive textual representations of the input news content. To effectively separate latent authenticity-related and event-specific knowledge within the news content, we employ a disentanglement architecture. To further enhance the decoupling effect, we introduce a cross-perturbation mechanism that perturbs authenticity-related representation with the event-specific one, and vice versa, deriving a robust and discerning authenticity-related signal. Additionally, we implement a refinement learning scheme to minimize potential interactions between two decoupled representations, ensuring that the authenticity signal remains strong and unaffected by event-specific details. Experimental results demonstrate that our approach effectively mitigates the impact of event-specific influence, outperforming state-of-the-art methods. In particular, it achieves a 6.0% improvement in accuracy on the PHEME dataset over MDDA, a similar approach that decouples latent content and style knowledge, in scenarios involving articles from unseen events different from the topics of the training set.

Federated learning (FL) is a distributed machine learning paradigm that ensures data do not leave local devices. Data sharing problems can be addressed by FL in untrusted environments, e.g., the Internet of Vehicles (IoV). However, FL needs to frequently exchange massive parameters to achieve preset model goals. In addition, the change in bandwidths and the delay of data communications due to user mobility challenge the synchronization of model parameters. In this article, an efficient hierarchical asynchronous FL (EHAFL) algorithm is proposed to adjust the encoding length dynamically according to the bandwidth and reduce the communication cost substantially. A dynamic hierarchical asynchronous aggregation mechanism is proposed leveraging gradient sparsification and asynchronous aggregation techniques to further reduce the communication costs and improve the aggregation efficiency of the global model. Simulation results on MNIST and real-world data sets show that our proposed solution can reduce the communication costs by 98% while only compromising the model accuracy by 1%.

In the study of information diffusion in social–physical networks, existing works are usually based on information entropy. These works measure and represent the information attribute characteristics independently for social networks and physical networks, resulting in ineffective interactions and waste of resources. Therefore, to solve the key problem of the mismatch between interaction demands and communication resources, the framework of collaborative diffusion based on value measurement is proposed in social–physical networks, including social–physical interaction, cognitive difference, and mutual trust degree of nodes. Based on parameterizing the relative strength of these influences by confidence and collaborative conservation factors, the collaborative diffusion model based on value measurement is established. Extensive simulations verify the influence of value measurement on the collaboration diffusion process, presented by the evolutions of value entropy, sentiment fragmentation, and diffusion range. In addition, the influence of collaboration on information dissemination is confirmed by the comparison of the change of value entropy and diffusion range. These results can help decision makers better balance the matching problem between interaction demands and available resources, which is beneficial to realize customized information diffusion.

As one of the essential paradigms of Industrial 4.0, the Industrial Internet of Things (IIoT) challenges existing data management and information services by supporting computational-intensive applications, in which devices share and receive information through interactions under resource constraints. When there exist diverse service requirements of IIoT applications, information dissemination will be more likely driven by service-oriented incentives. In this article, a novel information dissemination process with the service-oriented incentive mechanism is analyzed and modeled in IIoT, which depicts the dynamical evolution of IIoT devices’ interactions. In particular, the characteristics of service-oriented activating and dissemination degenerating are considered due to the unique capability of IIoT devices. Extensive theoretical and simulation results verify the dynamical behaviors of information dissemination, including the propagation threshold, equilibrium, and stability. In addition, comparative simulations have demonstrated the service-oriented incentive mechanism further expands information diffusion by driving the participation of IIoT devices.

The globalization process for integrated circuits (ICs) raises serious concerns regarding hardware Trojans (HTs). Due to the stealth and variety of HTs, detecting them at test time can be very resource intensive. This situation becomes even worse when the device under test (DUT) contains untrusted third-party intellectual property (3PIP) cores. For systems on chip (SoCs) that rely on untrusted 3PIP cores, this article solves the online HT detection and recovery problem from a graph-theoretic perspective. The proposed graph-theoretic models minimize the implementation cost of the system in terms of both the cost of purchasing different IP cores and the area overhead. To further enhance the security of the system, we also propose schemes to locate and replace an HT-infected IP core. The feasibility and efficiency of the proposed techniques are verified by experiments.

Abstract DevOps is a new software engineering paradigm adopted by various software organizations to develop the quality software within time and budget. The implementation of DevOps practices is critical, and there are no guidelines to assess and improve the DevOps activities in software organizations. Hence, there is a need to develop a readiness model for DevOps (RMDevOps) with an aim to assist the practitioners for implementation of DevOps practices in software firms. To achieve the study objective, we conducted a systematic literature review (SLR) study to identify the critical challenges and associated best practices of DevOps. A total of 18 challenges and 73 best practices were identified from the 69 primary studies. The identified challenges and best practices were further evaluated by conducting a survey with industry practitioners. The RMDevOps was developed based on other well‐established models in software engineering domain, for example, software process improvement readiness model (SPIRM) and software outsourcing vendor readiness model (SOVRM). Finally, case studies were conducted with three different organizations with an aim to validate the developed model. The results show that the RMDevOps is effective to assess and improve the DevOps practices in software organizations.

The bullet subtitle reflects a kind of instant feedback from the user to the current video. It is generally short but contains rich sentiment. However, the bullet subtitle has its own unique characteristics, and the effect of applying existing sentiment classification methods to the bullet subtitle sentiment classification problem is not ideal. First, since bullet subtitles usually contain a large number of buzzwords, existing sentiment lexicons are not applicable, we propose Chinese Bullet Subtitle Sentiment Lexicon on the basis of existing sentiment lexicons. Second, considering that some traditional affective computing methods only consider the text information and ignore the information of other dimensions, we construct a bullet subtitle affective computing method by combining the information of other dimensions of the bullet subtitle. Finally, aiming at the problem that existing classification algorithms ignore the importance of sentiment words in short texts, we propose a sentiment classification method based on affective computing and ensemble learning. Our experiment results show that the proposed method has higher accuracy and better practical application effect.

DevOps is a new software engineering paradigm adopted by various software organizations to develop an environment of continuous deployment and delivery within time. Numerous experts are offering their services to help organizations, how to implement DevOps activities in software organization. Though, still there are various issues for software organizations to adopt DevOps activities. To overcome such issues, there must be an approach that could assist software organizations towards better adoption of DevOps activities. The core objective of this research is to design a Readiness Model for DevOps (RMDevOps) to improve the adoption of DevOps activities in a software organization. Based on existing models in other fields of software engineering, we will develop this model. We have conducted a systematic literature review and empirical study on DevOps, for understanding the impact of the success factors of DevOps in the real world and literature. This study covers the first step of development of RMDevOps model, by identifying the success factors of DevOps and presenting the outcomes in the form of robust framework.

Security in DevOps is a challenging feature because traditional existing methods of security are not fulfilling the exact requirements of DevOps. To make DevOps activities successful for organizations this study will help to identify concerns about DevOps security in real world by interviewing the practitioners having DevOps working experience. The Classical grounded theory approach has been used to build our theory. We interviewed 13 practitioners working across five companies from different regions. We contributed to identify security concerns in DevOps and try to present a theoretical model to improve understanding and guidance of DevOps adoption. The security concerns were marked as functional and nonfunctional based upon the interviews concepts to help practitioners having understanding about particular concerns. Therefore, this theory will highlight security concerns that are hindering the adoption of DevOps successfully.

Abstract In this paper, we introduce a novel particle filtering architecture to simulate uncertain emotion generation under multi‐stimulus, to enrich emotions for virtual characters. Particles are exploited to predict emotions by sampling possible natural emotional reactions from individuals' memories and common reactions, and the prediction is subsequently adjusted through likelihood function constructed through appraisals of the cognitive component. Thus this generation process combines the natural reactions and cognitive results of individuals' into a unified architecture. Furthermore, the expression of emotion as a communicative act is implemented by setting moral standards that an individual must obey, no matter what his or her real emotional reaction is to the stimulus. This generation and expression process is implemented with our uncertain emotion generator under multi‐stimulus system (UEGM). Copyright © 2011 John Wiley & Sons, Ltd.

Clustering is an important technique in data mining. In this paper, we introduce a new clustering algorithm. This algorithm, based on granular computing, constructs a hypergraph (simplicial complex) by the hypergraph bisection algorithm. It will discover the similarities and associations among documents. In some experiments on Web data, the proposed algorithm is used; the results are quite satisfactory. © 2009 Wiley Periodicals, Inc.

Since Instant Messaging Network is one of the most important ways to propagate information, in order to monitor and forecast the propagation behaviors on Instant Messaging Network, it is necessary to study information propagation nature, discipline and methods. Scale-free network with tunable clustering can be used to build instant messaging. Based on it, we propose a new two-layer model of Instant Messaging Network, and formulate its information propagation rules as well. We have simulated this model and observed the diversification of group numbers, clustering coefficient, spreader and stifler. Results show that the more the number of group network is, the larger cluster coefficient and the higher maximum spreader and the higher final value of stifler are. Thus group network and cluster coefficient effect the information propagation.