Semi-supervised learning via DQN for log anomaly detection

Log anomaly detection is a critical component in modern software system security and maintenance, serving as a crucial support and basis for system monitoring, operation, and troubleshooting. It aids operations personnel in timely identification and resolution of issues. However, current methods in log anomaly detection still face challenges such as underutilization of unlabeled data, imbalance between normal and anomaly class data, and high rates of false positives and false negatives, leading to insufficient effectiveness in anomaly recognition. In this study, we propose a semi-supervised log anomaly detection method named DQNLog, which integrates deep reinforcement learning to enhance anomaly detection performance by leveraging a small amount of labeled data and large-scale unlabeled data. To address issues of imbalanced data and insufficient labeling, we design a state transition function biased towards anomalies based on cosine similarity, aiming to capture semantic-similar anomalies rather than favoring the majority class. To enhance the model's capability in learning anomalies, we devise a joint reward function that encourages the model to utilize labeled anomalies and explore unlabeled anomalies, thereby reducing false positives and false negatives. Additionally, to prevent the model from deviating from normal trajectories due to misestimation, we introduce a regularization term in the loss function to ensure the model retains prior knowledge during updates. We evaluate DQNLog on three widely used datasets, demonstrating its ability to effectively utilize large-scale unlabeled data and achieve promising results across all experimental datasets

The Role of CD 133+ Cells in a Recurrent Embryonal Tumor with Abundant Neuropil and True Rosettes ( ETANTR )

Embryonal tumor with abundant neuropil and true rosettes ( ETANTR ) is a recently described embryonal neoplasm of the central nervous system, consisting of a well‐circumscribed embryonal tumor of infancy with mixed features of ependymoblastoma (multilayer ependymoblastic rosettes and pseudorosettes) and neuroblastoma (neuroblastic rosettes) in the presence of neuropil‐like islands. We present the case of a young child with a very aggressive tumor that rapidly recurred after gross total resection, chemotherapy and radiation. Prominent vascular sclerosis and circumscribed tumor led to the diagnosis of malignant astroblastoma; however, rapid recurrence and progression of this large tumor after gross total resection prompted review of the original pathology. ETANTR is histologically distinct with focal glial fibrillary acid protein ( GFAP ) and synaptophysin expression in the presence of neuronal and ependymoblastic rosettes with focal neuropil islands. These architectural features, combined with unique chromosome 19q13.42 amplification, confirmed the diagnosis. In this report, we describe tumor stem cell ( TSC ) marker CD 133, CD 15 and nestin alterations in ETANTR before and after chemotherapy. We found that TSC marker CD 133 was richly expressed after chemotherapy in recurrent ETANTR , while CD 15 is depleted compared with that expressed in the original tumor, suggesting that CD 133+ cells likely survived initial treatment, further contributing to formation of the recurrent tumor.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102077/1/bpa12079.pd

Energy-Dissipation Performance of Combined Low Yield Point Steel Plate Damper Based on Topology Optimization and Its Application in Structural Control

In view of the disadvantages such as higher yield stress and inadequate adjustability, a combined low yield point steel plate damper involving low yield point steel plates and common steel plates is proposed. Three types of combined plate dampers with new hollow shapes are proposed, and the specific forms include interior hollow, boundary hollow, and ellipse hollow. The “maximum stiffness” and “full stress state” are used as the optimization objectives, and the topology optimization of different hollow forms by alternating optimization method is to obtain the optimal shape. Various combined steel plate dampers are calculated by finite element simulation, the results indicate that the initial stiffness of the boundary optimized damper and interior optimized damper is lager, the hysteresis curves are full, and there is no stress concentration. These two types of optimization models made in different materials rations are studied by numerical simulation, and the adjustability of yield stress of these combined dampers is verified. The nonlinear dynamic responses, seismic capacity, and damping effect of steel frame structures with different combined dampers are analyzed. The results show that the boundary optimized damper has better energy-dissipation capacity and is suitable for engineering application

A Multi-objective Stochastic Programming Model for Order Quantity Allocation under Supply Uncertainty

One of the basic and significant subjects in supply chain management is purchasing and supply management, in which supplier selection and order allocation occupy the critical position. Recently, it has been shown that supply uncertainty is of great concern to supply chain managers and practitioners. In this paper, by taking the constraints of minimum purchasing quota and minimum production batch into account, a multi-objective mixed-integer stochastic programming model considering uncertainty in both supply timing and quantity is presented. By means of transforming the stochastic constraints into deterministic equivalents, the model is converted into a linear programming model. An improved two-phase heuristic approach is proposed and its feasibility and efficiency is illustrated through a numerical example. Further, another numerical instance is conducted to evaluate the effects of the weight of each objective and uncertainty degree on the optimal order policy and to obtain some managerial insights for the decision-making of the manufacturers

Threat Assessment for Multistage Cyber Attacks in Smart Grid Communication Networks

In smart grids, managing and controlling power operations are supported by information and communication technology (ICT) and supervisory control and data acquisition (SCADA) systems. The increasing adoption of new ICT assets in smart grids is making smart grids vulnerable to cyber threats, as well as raising numerous concerns about the adequacy of current security approaches. As a single act of penetration is often not sufficient for an attacker to achieve his/her goal, multistage cyber attacks may occur. Due to the interdependence between the power grid and the communication network, a multistage cyber attack not only affects the cyber system but impacts the physical system. This thesis investigates an application-oriented stochastic game-theoretic cyber threat assessment framework, which is strongly related to the information security risk management process as standardized in ISO/IEC 27005. The proposed cyber threat assessment framework seeks to address the specific challenges (e.g., dynamic changing attack scenarios and understanding cascading effects) when performing threat assessments for multistage cyber attacks in smart grid communication networks. The thesis looks at the stochastic and dynamic nature of multistage cyber attacks in smart grid use cases and develops a stochastic game-theoretic model to capture the interactions of the attacker and the defender in multistage attack scenarios. To provide a flexible and practical payoff formulation for the designed stochastic game-theoretic model, this thesis presents a mathematical analysis of cascading failure propagation (including both interdependency cascading failure propagation and node overloading cascading failure propagation) in smart grids. In addition, the thesis quantifies the characterizations of disruptive effects of cyber attacks on physical power grids. Furthermore, this thesis discusses, in detail, the ingredients of the developed stochastic game-theoretic model and presents the implementation steps of the investigated stochastic game-theoretic cyber threat assessment framework. An application of the proposed cyber threat assessment framework for evaluating a demonstrated multistage cyber attack scenario in smart grids is shown. The cyber threat assessment framework can be integrated into an existing risk management process, such as ISO 27000, or applied as a standalone threat assessment process in smart grid use cases

BGI-RIS: An integrated information resource and comparative analysis workbench for rice genomics

Rice is a major food staple for the world's population and serves as a model species in cereal genome research. The Beijing Genomics Institute (BGI) has long been devoting itself to sequencing, information analysis and biological research of the rice and other crop genomes. In order to facilitate the application of the rice genomic information and to provide a foundation for functional and evolutionary studies of other important cereal crops, we implemented our Rice Information System (BGI-RIS), the most up-to-date integrated information resource as well as a workbench for comparative genomic analysis. In addition to comprehensive data from Oryza sativa L. ssp. indica sequenced by BGI, BGI-RIS also hosts carefully curated genome information from Oryza sativa L. ssp. japonica and EST sequences available from other cereal crops. In this resource, sequence contigs of indica (93-11) have been further assembled into Mbp-sized scaffolds and anchored onto the rice chromosomes referenced to physical/genetic markers, cDNAs and BAC-end sequences. We have annotated the rice genomes for gene content, repetitive elements, gene duplications (tandem and segmental) and single nucleotide polymorphisms between rice subspecies. Designed as a basic platform, BGI-RIS presents the sequenced genomes and related information in systematic and graphical ways for the convenience of in-depth comparative studie

Precision marketing method and strategy of e-commerce platform based on clustering algorithm

In the ever-changing community e-commerce, it is crucial to comprehend the complex connection between quality of a community e-commerce platform and the behavior of consumers in order to maintain growth and competitiveness. This study empirically examines the influence of community e-commerce platforms’ quality, including system quality, information quality, and service quality, on consumers' intention to repurchase, with a specific focus on daily necessities. Stimulus-Organism-Response (SOR) paradigm is adopted to examine how the perception of hedonic and utilitarian values influence the connection between platform quality and repurchase intentions. The results of an online survey conducted with 181 users of community e-commerce platforms demonstrate significant impact of system quality and service quality on repurchase intention. This study particularly emphasizes the key role of the clustering algorithm in precision marketing. Clustering algorithm helps to organize and collect user behavior models and related information, and segment consumer groups, thus laying a solid foundation for precision marketing. During the process of users segmentation, it explores the similarities and differences of different groups, combines market and product development strategies, and targets the selection and integrattion of user markets. This improves the accuracy of market strategies, ensures the full implementation of consumer-ccentric service concept, fully meets the needs of different consumer groups, tracks the status of target customers in real-time, and carries out marketing work.This enhancement is predominantly mediated by perceived hedonic and utilitarian values, highlighting their crucial importance in the process of consumer decision-making. The study also reveals that the impact of information quality on repurchase intentions is limited, and its indirect effect through perceived utilitarian value is also minimal. This sheds light on the complex relationship between platform quality and consumer perceptions. This study enriches the existing literature by providing a detailed understanding of the factors that influence customer loyalty in the digital market. It also provides strategic advice to community ecommerce platforms that seek to increase consumer engagement and enhance their competitive position by implementing qualitative improvements in systems, services, and information distribution

Dynamic metrics-based biomarkers to predict responders to anti-PD-1 immunotherapy

Background Anti-PD-1 immunotherapies have shown clinical benefit in multiple cancers, but response was only observed in a subset of patients. Predicting which patients will respond is an urgent clinical need, but current companion diagnosis based on PD-L1 IHC staining shows limited predictability. Methods A dynamic, metrics-based biomarker was developed to discriminate responders from non-responders for anti-PD-1 immunotherapy in B16F10 melanoma-bearing mice. Results Similar to patients, there was considerable heterogeneity in response to anti-PD-1 immunotherapy in mice. Compared with the control group, 45% of anti-PD-1 antibody-treated mice displayed improved survival (defined as responders) and the remainder only gained little, if any, survival benefit from PD-1 blockade (non-responders). Interestingly, the dynamics of IFN-γ secretion by peripheral lymphocytes was associated with faster secretion onset (shorter lag time), stronger exponential phase, shorter time to half magnitude, and higher magnitude of secretion in responders at day 10 after tumour inoculation. To sufficiently predict responders from non-responders, IFN-γ secretion descriptors as well as phenotypic markers were subjected to multivariate analysis using orthogonal partial least-squares discriminant analysis (OPLS-DA). Conclusions By integrating phenotypic markers, IFN-γ secretion descriptors sufficiently predict response to anti-PD-1 immunotherapy. Such a dynamic, metrics-based biomarker holds high diagnostic potential for anti-PD-1 checkpoint immunotherapy