哈尔滨信息工程学院始建于1995年，是经教育部批准设置的全日制普通本科高等学校，是国家示范性软件技术学院、全国高校毕业生就业工作先进单位。学院两个校区分别坐落在高校林立的哈尔滨利民开发区学院路和哈东新区大学城。校园占地面积70万平方米，建筑面积20.4万平方米，教学仪器设备总值4280万元，图书68万余册，实现了校园内无线网络全覆盖，生均占有资源位于同类院校前列。截止到2015年9月30日，在校生6753人，其中本科在校生1542人，占学生总数的22.83%。学院现有专任教师382人，生师比17.68:1；硕士以上学历222人，占专任教师总数的58.12 %；副高级及以上职称150人，其中教授51人，副高级及以上职称比例达到39.27%；本科生主讲教师全部达到硕士以上学历，副教授以上教师承担本科教学任务达72%，教授承担本科课程比例达40%。
The current intrusion detection methods suffer from deficiencies in terms of cross-domain adaptability, privacy preservation, and limited effectiveness in detecting minority-class attacks. To address these issues, a novel intrusion detection model framework, TrMulS, is proposed that integrates federated learning, generative adversarial networks with multispace feature enhancement ability, and transformers with multi-source transfer ability. First, at each institution (source domain), local spatial features are extracted through a CNN, multiple subsets are constructed (to solve the feature singularity problem), and the multihead self-attention mechanism of the transformer is utilized to capture the correlation of features. Second, the synthetic samples of the target domain are generated on the basis of the improved Exchange-GAN, and the cross-domain transfer module is designed by combining the Maximum Mean Discrepancy (MMD) to minimize the feature distribution difference between the source domain and the target domain. Finally, the federated transfer learning strategy is adopted. The model parameters of each local institution are encrypted and uploaded to the target server and then aggregated to generate the global model. These steps iterate until convergence, yielding the globally optimal model. Experiments on the ISCX2012, KDD99 and NSL-KDD intrusion detection standard datasets show that the detection accuracy of this method is significantly improved in cross-domain scenarios. This paper presents a novel paradigm for cross-domain security intelligence analysis that considers efficiency, privacy and balance.
With the continuous development of intelligent manufacturing, tool condition monitoring has become a key link in ensuring machining quality and stable equipment operation. This paper conducts research on tool anomaly detection in the field of intelligent manufacturing based on the lightweight object detection algorithm You Only Look Once v11 (YOLOv11). By collecting multiple types of tool anomaly images through high-definition cameras, precise bounding box annotations of anomaly regions are performed using Labelme and COCO-annotator tools. A dataset compliant with YOLOv11 specifications is constructed and reasonably divided, thereby completing the model's training, inference, and postprocessing. Experimental results show that the proposed method performs excellently in both detection accuracy and real-time performance, demonstrating good industrial application value and promotion prospects.
Driven by the rapid expansion of the digital economy, global business models and employment markets have imposed novel competency requirements on Business English professionals. Distinct from traditional evaluation systems centered on linguistic skills, this paper aims to construct a scientific and systematic evaluation model for the vocational competence of Business English talent within the context of the digital economy. The study employs the Analytic Hierarchy Process (AHP) to establish a three-tier evaluation system comprising a target layer, a criterion layer, and an indicator layer. The results indicate that all judgment matrices passed the consistency test, clarifying the weight distribution of various capability indicators. This research provides a quantitative basis for cultivating high-quality Business English talents who align with the demands of the digital era.
