Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent data with the generative capabilities of Large Language Models (LLMs), ensuring that the generated output is not only contextually relevant but also accurate and current. We introduce XRAG, an open-source, modular codebase that facilitates exhaustive evaluation of the performance of foundational components of advanced RAG modules. These components are systematically categorized into four core phases: pre-retrieval, retrieval, post-retrieval, and generation. We systematically analyse them across reconfigured datasets, providing a comprehensive benchmark for their effectiveness. As the complexity of RAG systems continues to escalate, we underscore the critical need to identify potential failure points in RAG systems. We formulate a suite of experimental methodologies and diagnostic testing protocols to dissect the failure points inherent in RAG engineering. Subsequently, we proffer bespoke solutions aimed at bolstering the overall performance of these modules. Our work thoroughly evaluates the performance of advanced core components in RAG systems, providing insights into optimizations for prevalent failure points.

Timeline summarization (TLS) system involves creating a concise overview of extended daily summaries through sentence and date selection, employing predefined unlearnable representations for these elements. This poses a unique challenge, as it involves normal representations that limit the performance of the downstream daily summarization tasks. Previous research in this field separately encoded dates and sentences in independent learning representation spaces, hindering the integration of their relationships and the attainment of a globally optimal summary by the TLS system. In this study, our emphasis is on pretraining a document encoder for the TLS system, where we introduce the concept of representing each document as a plug-and-play document plugin (PlugIN). Specifically, the proposed MegraTLS (Meta-graph autoencoder based TimeLine Summarization System) is based on a modified meta-graph autoencoder network. By incorporating the prefabricated PlugIN into the backbone of theDateWiseTLS framework for downstream timeline summarization, we streamline the encoding of a document, enabling efficient handling of date and sentence selection within a compact representation space in date and sentence embeddings. The experimental results demonstrate that our MegraTLS prominently promotes existing TLS frameworks in timeline summarization performance. Besides, the MegraTLS exhibits robustness and insensitivity to parameter changes. Case studies of financial events illustrate that our model accurately captures key timeline events when applied to specific fields, providing more industry-specific valuable information than ground truth while maintaining simplicity. Although our experiments are conducted exclusively on Chinese datasets, the methodological insights and empirical findings of this work offer transferable, actionable, and robust guidance for deploying timeline summarization technology in domain-specific industrial applications.

Pareto Front Learning (PFL) has been one of the effective means to resolve multi-objective optimization problems through exploring all optimal solutions to learn the entire Pareto front. Pareto Hypernetwork (PHN) is a new promising way to generate the sequence of Pareto-optimal solutions that can be further used as potential solutions to constitute the Pareto front. However, the existing PHN-based approaches suffer from two performance issues: They take as inputs human-crafted preference vector or chunk embedding, rather than the input data samples, and thus vulnerable to data distribution shifts. Such approaches cannot optimize all potential solutions when forming the Pareto front, as they merely optimize the loss pertaining to one single input at a time of optimization round. To improve the quality of the Pareto front, we propose IOP, a novel Idempotent-like Optimization method to learn the entire Pareto front accurately and enhance Hypernetwork's adaptability to distribution shifts. In particular, IOP performs idempotent-like optimization by exploiting manifold space mapping, so that the target networks generated by the optimized Hypernetwork can effectively handle samples with similar distributions of the input samples, without the pre-defined human-crafted inputs. IOP maximizes the Hypervolume indicator that is composed of all potential solutions at a higher level. Experimental results demonstrate that IOP outperforms the state-of-the-art methods by 4.7% on average in producing the Pareto front and has a 10.5% improvement in adaptability.

Disk failure prediction is crucial for enhancing data reliability. Achieving high-precision prediction requires effectively capturing “dynamic events”—abrupt temporal changes in disk attribute values—and deeply understanding “static states”—the complex synergistic relationships among multi-dimensional features. To this end, we propose DTAFormer, a novel deep learning model that fuses temporal change perception with dual-dimension attention for disk failure prediction. DTAFormer forecasts failures by processing two key information streams in parallel: it employs a strategy of adaptive thresholding combined with persistence validation to capture “dynamic events”, while leveraging a dual-dimension attention mechanism to profoundly explore “static states”. Ultimately, a novel cross-gated attention mechanism facilitates robust and efficient integration of these two heterogeneous information types. Extensive experiments on the real-world Backblaze dataset demonstrate that DTAFormer achieves an average F1-score of 96.90%, outperforming the state-of-the-art method by 18.9%.

Continuous learning faces the challenge of catastrophic forgetting. Our research findings indicate that in unsupervised federated continual learning (UFCL), the limited model capacity and interference among participants are the key factors contributing to this problem. Specifically, the fixed capacity of the model restricts its ability to retain historical knowledge. Besides, the indiscriminate aggregation of weights from multiple participants can cause interference, damaging the model memory. To address these challenges, we propose FedFRR, a federated anti-forgetting representation learning approach. FedFRR fits the participants’ data distribution through a weighted combination of primary network units (PNU) in the model and optimizes model memory by adjusting the structure of PNUs. Additionally, FedFRR addresses interference by truncating the PNU with less weight change, thus reducing the scope of weight aggregation. The experimental results demonstrate that FedFRR achieves state-of-the-art performance, significantly enhancing the model’s anti-forgetting ability.

Time-series Anomaly Detection(AD) is widely used in monitoring and security applications in various industries and has become a hot spot in the field of deep learning. Normality-representation-based methods perform well in certain scenarios but may ignore some aspects of the overall normality. Feature-extraction-based methods always take a process of pre-training, whose target differs from AD, leading to a decline in AD performance. In this paper, we propose a new AD method called deep Autoencoding One-Class (AOC), which learns features with AutoEncoder(AE). Meanwhile, the normal context vectors from AE are constrained into a hypersphere small enough, similar to one-class methods. With an objective function that optimizes the two assumptions simultaneously, AOC learns various aspects of normality, which is more effective for AD. Experiments on public datasets show that our method outperforms existing baseline approaches.

In federated learning, the non-IID data generated from heterogeneous clients may reduce the global model efficiency. Previous studies use personalization as a common approach to adapt the global model to these clients (called the local model). However, client’s data distribution may change dynamically with its location or environment, which can degrade the performance of the local model, leading to a new Dynamic Personalized Federated Learning (DPFL) problem. This paper proposes a novel approach to reduce the impact of the dynamic distribution on the local model based on metalearning and distribution distance measurement named Fed-3DA. It calculates the distribution distance periodically to perceive the distribution change on the client and adjust the local model preferences from a global meta-model through the distribution representation. Our experiments on public datasets show that Fed-3DA can effectively reduce the performance fluctuation of the local model in DPFL scenarios.

Software Defined Network (SDN) enables network operators to achieve the customization of network services, which tends to be more dynamic and fine-grained. However, the distributed nature of rule updating in SDN brings consistency problems, i.e., packets travel according to different versions of rules. It leads to the issues of blackholes, loops, congestion, and deadlock in the data plane, which may further affect the service quality of the application plane. With the emergence of new computing paradigms such as edge computing and fog computing, the heterogeneity of network devices and links, as well as the diversity of network application requirements have become increasingly prominent. Traditional update methods ignore these key factors when modeling, so they cannot cope with the increasingly complex network environment, resulting in delays or packet loss rates that do not meet service requirements. This paper proposes HyCU, which takes device performance as a constraint and optimizes updates based on flow service requirements. We conduct experiments under different scenarios and constraints over two real-world topologies with real-time running flows, demonstrating the effectiveness of HyCU.

With the development of industrial automation and intelligence deepening, industrial robots' installations increase rapidly. The traditional robot operation and management methods are no longer suitable for current requirements. Cloud computing and big data technology together are deemed to form a promising and possible solution to this challenge. From this point of view, this paper proposes a novel industrial robotics cloud platform architecture, and application orchestration and containerized software appliance technology-enabled cloud-edge collaboration mechanism is designed therein. Two exemplary and real-world case studies, supervisory control and data acquisition, and industrial robot predictive maintenance, are further conducted to demonstrate our architecture's generality and versatility.

Hybrid clouds require scalable, high performance virtual networks (VN) to facilitate the resource interoperability among multiple clouds. However, current VNs in hybrid clouds face the following challenges: (1) Management complexity. The VNs in hybrid clouds need to support inter-cloud resource deployment with multi-tenancy and full address space virtualization, and therefore are complex to manage. Meanwhile, the VNs are vulnerable to failures due to their large scale. (2) Performance limitation. Hybrid clouds rely on the Internet to support inter-cloud data transmission. Therefore without a meticulous design, the inter-cloud VNs would suffer from a performance limitation introduced by the Internet, e.g., the intercloud bandwidth provided by the Internet is much lower and more fluctuant than that of the intra-cloud networks. To the best of our knowledge, most existing VN solutions cannot solve both of these challenges simultaneously.In this paper we introduce HCNet, a hybrid cloud virtual network management system. HCNet makes use of Software Defined Networking (SDN) and: (1) supports multi-tenancy and full address space virtualization on both Layer-2 and Layer-3 networks through the help of its elaborated packet processing pipeline, (2) leverages a dynamic load balance policy atop multiple inter-cloud gateways to achieve better performance on inter-cloud virtual networking, (3) maintains master-slave backup relationship based on paxos protocol to deal with controller outages. The experiment results indicate that HCNet achieves significant inter-cloud communication performance gain with respect to packet loss. Moreover, in presence of controller failures, HCNet is able to recover in about 5 seconds.