Log anomaly detection (LAD) is of paramount importance to enhance the reliability and stability of software systems. Current state-of-the-art LAD suffers a significant performance degradation when dealing with consistently evolving log events caused by system updates. To build a reliable LAD model under the context of log data evolution, we propose an incremental learning-based method for LAD, namely ilLog, to avoid catastrophic forgetting of previously learned knowledge while continuously updating the model for better detection when processing the evolving log events. In particular, we design a novel entropy-driven sorting algorithm for real log sample replay, which enables the preservation of old knowledge via storing representative samples with discrete sequence features from previous tasks. Additionally, we introduce a Halton-based low discrepancy sequence to better approximate the sliced Cram´ er distance between the probability distributions of two models, thus enhancing the model learning capability. Based on a standard incremental learning protocol setting, we evaluate the newly proposed ilLog method on three publicly available datasets. Experimental results demonstrate that our approach achieves the best performance compared to SOTA LAD methods and models by applying existing IL-based methods in evolving software systems.

Adverse weather causes diverse and complex image degradations, severely compromising the reliability of computer vision systems. Existing all-in-one restoration models attempt to address multiple degradation types within a unified framework, but often lack explicit spatial and semantic modeling of degradation characteristics, limiting their adaptability to diverse weather conditions. To address this limitation, we propose a Degradation-Aware Cross-Modal Prompt Compensation Network (DCMPC-Net) that leverages cross-modal degradation cues from a pre-trained vision-language model to condition restoration features within a unified backbone. Specifically, our DCMPC-Net mainly consists of the Cross-Modal Prompt Generator (CMPG), Prompt-Guided Attention Alignment Module (PGAAM), and Dual Feature Compensation Module (DFCM). The CMPG integrates textual embeddings with visual features to produce degradation-aware prompts that encode degradation-related semantic and contextual cues. These prompts are injected into the decoder via a PGAAM, which adaptively aligns semantic information with degraded regions to facilitate context-aware restoration. To further enhance structural fidelity, DFCM is introduced that disentangles degradation artifacts from scene structures, thereby improving the reconstruction of fine textures and detailed content. By integrating cross-modal semantic guidance with spatial alignment and structural enhancement, DCMPC-Net achieves robust and perceptually consistent restoration across diverse weather conditions. Extensive experiments show that DCMPC-Net outperforms state-of-the-art methods in both task-specific and unified settings, achieving superior accuracy and visual fidelity. The code is available at https://github.com/fanamber831/DCMPC-Net.

Abstract Named entity recognition (NER) is a critical task in natural language processing. It extracts entity information such as person, location, and organization by predicting various categories of label types and entity spans in text. Nowadays, NER has achieved good recognition results in English text by machine learning. However, satisfactory recognition results cannot be achieved when processing text in Japanese, due to the diversity of the text composition and the particularity of the language itself. Compared with English text, which different words are marked by spaces, there is no clear separation mark between two words in Japanese. Simultaneously, Japanese text includes three types of representation methods, which is different from English text which only consists of English alphabet. In order to solve the above problems, a feature integration network with BERT called FINB is introduced in this paper based on multi-feature integration, which can integrate pronunciation features and glyph features of Japanese into the model to obtain more semantic information. The experiments for verification are conducted on the Kyoto University Web Document Leads Corpus called KWDLC and the Japanese Wikipedia dataset, which both prove that the proposed method can improve the recognition of named entities in Japanese effectively.

Log anomaly detection (LAD) is essential to ensure the safe and stable operation of Cyeber-physical systems. Although current LAD methods exhibit significant potential in addressing challenges posed by unstable log events and temporal sequence patterns, their limitations in detection efficiency and generalization ability present a formidable challenge when dealing with evolving systems. To construct a real-time and reliable online log anomaly detection model, we propose OMLog, a semi-supervised online meta-learning method, to effectively tackle the distribution shift issue caused by changes in log event types and frequencies. Specifically, we introduce a maximum mean discrepancy-based distribution shift detection method to identify distribution changes in unseen log sequences. Depending on the identified distribution gap, the method can automatically trigger online fine-grained detection or offline fast inference. Furthermore, we design an online learning mechanism based on meta-learning, which can effectively learn the highly repetitive patterns of log sequences in the feature space, thereby enhancing the generalization ability of the model to evolving data. Extensive experiments conducted on two publicly available log datasets, HDFS and BGL, validate the effectiveness of the OMLog approach. When trained using only normal log sequences, the proposed approach achieves the F1-Score of 93.7% and 64.9%, respectively, surpassing the performance of the state-of-the-art (SOTA) LAD methods and demonstrating superior detection efficiency.

Log-based anomaly detection (LAD) is one of the dominant approaches to improving the reliability and security of software systems. Presently, despite the efficacy demonstrated by state-of-the-art LAD approaches in processing static log events, their performance significantly degrades when confronting changes of log event types from system updates. To construct a reliable LAD model that could adapt well to the evolution of log data, we propose a method grounded in semi-supervised domain adaptation on the rationale of incremental log anomaly detection dubbed as SSDALog, which dynamically updates the model utilizing limited labeled samples to reconcile distributional shifts between evolving and historical data. Specifically, the proposed approach addresses the issue through two primary mechanisms: (i) creation of a cross-domain mixup algorithm, which computes the feature salience of log discrete sequences through occlusion strategy, thus enhancing the adaptability of the model to unknown patterns by mixing evolving features; and (ii) design of an incremental semi-supervised domain adaptation training framework based on noisy label learning to obtain a robust feature extractor, thus improving the generalization ability of the detection model. We empirically assess the efficacy of the SSDALog approach across two publicly available datasets. The experimental results show that our method outperforms the SOTA LAD approach, particularly for evolving systems.

Identifying essential proteins is vital for deciphering the intricacies of disease mechanisms and devising efficacious therapeutic strategies. Over the past several decades, a plethora of algorithms have been proposed, aimed at synthesizing topological and biological information to address the complex challenge of essential protein identification. Nevertheless, a critical examination of the current methodologies reveals certain limitations: (1) the aggregation of diverse features in various methods often requires parameter tuning to maintain balance, potentially introducing instability and increasing complexity in practical scenarios; (2) traditional methods commonly combine various features without in-depth mathematical or physical interpretation, possibly falling short of fully revealing the principles behind the observed phenomena. Hence, we propose a new algorithm for essential protein detection, which is named ITBSE. The basic idea behind this method is to reconstruct the PPI network by removing false positive edges and the subsequent allocation of a protein score. This scoring process integrates both the topological attributes of the reconstructed PPI network and biological data, utilizing the computational framework of Shannon entropy for a comprehensive assessment. To evaluate the effectiveness of our method, we conduct the experiments on real PPI networks and compare with 10 popular methods including DC, BC, CC, LID, PR, DMNC, LAC, NC, PeC and esPOS. The comparison results demonstrate that ITBSE is able to achieve better performance than those competing algorithms.

Network traffic prediction is essential for network management and resource scheduling within Web information systems. However, existing prediction methods have difficulty fitting mutation values in traffic time-series data and are still inadequate in terms of precision. Here we describe a method for prediction using multimodal web traffic data. The method creates multi-dimensional time series on request traffic, response traffic, and abnormal code traffic, and uses the rich information contained in the different sequences in the preceding time window to make inferences about the traffic scale in subsequent time windows. In addition, we propose an improved algorithm based on the Gated Recurrent Unit (GRU) to reduce the prediction error. The algorithm introduces the residual structure into a stacked multi-layer recurrent network structure and uses the Mogrifier structure to interact the information before it is fed to the gating unit. The experimental results show that the improved method leads to a further reduction in the error between the predicted and true values, providing high usability in the field of network traffic prediction.

With the rapid development of social security service, people have higher demands for medical service. Therefore, it is the development direction of mobile medical construction to build humanized and personalized service concepts. Mobile Health is of great significance to the society and health. Because of the slow running and unsupported distribution, the traditional computer can't solve the current issue of big data processing in healthcare industry. Mobile medical based on mobile cloud computing environment can be a good solution to these problems. The research of k Nearest Neighbor (kNN) query in mobile cloud computing environments has become a popular topic. Supporting scalable and distributed spatial data indexes has a great impact on the efficiency of KNN queries. The existing query methods are not suitable for parallelization or lead to redundancy of content. In this paper, firstly, we introduce an inverted Voronoi based kNN query processing with MapReduce. Such a query tends to be applied in various fields including m-health treatment where medical services can be provided by hospitals, clinics and other medical institutions to meet more people's medical requirements in time. Secondly, we introduce a mobile-health call system based on kNN query. Thirdly, we build a novel efficient spatial data index: Inverted Voronoi Index, which contains both inverted index and Voronoi graph. Finally, we present the outcomes of extensive experiment that are gained by both real and simulated data sets which indicate efficiency and scalability of the proposed approach.

Contrary information is recorded in the process of Ribonucleic Acid transcription, which is similar to the anti-synchronization process. In this paper, the anti-synchronization of coupled Boolean networks is investigated. Firstly, the definition of anti-synchronization is introduced, and the drive-response Boolean networks dynamics are translated into linear representations with the semi-tensor product. Then, a necessary and sufficient condition is obtained for anti-synchronization of coupled Boolean networks. Finally, simulation example is presented to further illustrate the efficiency of the proposed method.

Gram staining is a traditional bacteriological laboratory technique, which has widely usage on many medical research and application. However, gram staining reading is a time consumption work. In this paper, we employ Convolutional Neural Network method to design a classifier, by which gram staining images can be identified as normal group and disease model group effectively and correctly. And image generate method is used to improve classification accuracy. The fecal gram stain smears of health rats and irritable bowel syndrome model rats are employed to validate the proposed model. The experiment results shown that our method can reach a validation accuracy of 95.6%. The method proposed in this paper can provide a quick and objective judgement for medical researchers or clinical diagnosis.