面向人工智能的军队网络安全试验鉴定研究

信息网络技术作为武器装备的力量倍增器,在现代战争中地位与作用凸显,防止针对信息网络系统脆弱性的攻击,确保武器装备网络安全也变得尤为重要,试验鉴定技术是确保网络安全的重要手段,而随着大数据、人工智能等技术快速发展,基于智能化的网络安全与攻防技术对国家、军队信息网络安全提出了全新的挑战,也对试验鉴定工作提出更高要求。本文首先对“网络安全试验鉴定”概念进行阐述;其次,探讨了美军网络安全试验鉴定工作的内容与流程,最后,从人工智能为网络安全试验鉴定能力带来的重大挑战,引申出我军发展人工智能网络安全试验鉴定的几点做法和建议。

地震现场建筑物安全鉴定结构模型精细化研究及网络版系统框架设计

在较强地震发生后的应急期,对受震建筑在预定功能下的使用安全性和可居住性进行鉴定是地震现场应急工作的重要组成部分,是妥善安置灾民生活、恢复社会秩序、科学有序救灾的有效途径。目前我国地震现场的安全鉴定工作的开展主要依靠专家经验评估。作为此项工作的参考,标准《地震现场工作第二部分：建筑物安全鉴定》（GB18208.2—2001）只给出定性条款。

红木识别鉴定平台的设计与实现

介绍了基于sql语句的红木树种和红木家具网络鉴定平台的设计与实现过程。该平台既可交流和传递红木知识及研究成果,又能以最简单、快捷的方式鉴定红木家具的真假。

“智慧+”模式下司法鉴定发展路径探索

智慧侦查、智慧检察、智慧法院的诞生和成长说明,“智慧+”模式在法学领域的应用已初见成效,其与司法鉴定的融合指日可待。智慧司法鉴定是依托大数据理念和思维,运用互联网、云计算、深度学习等技术手段,以海量数据为支撑的鉴定委托和材料补充智慧化、鉴定管理智慧化、鉴定实施过程智慧化和鉴定质证智慧化。通过浅析司法鉴定发展历程、解读智慧司法鉴定的内涵以及总结智慧司法鉴定的初步探索,最终提出未来智慧司法鉴定的可能发展路径。

我国司法鉴定现状分析及完善管理机制的建议

逐一剖析对我国目前司法鉴定的市场化倾向、皮包公司式鉴定机构模式、统一管理问题,希望尽快完善相关法律,进一步确立统一管理体制,期待及早构建四级司法鉴定网络,促进司法鉴定体制改革健康快速发展。

电子证据也算数——道家电子数据司法鉴定中心正式成立

1月25日，北京网络行业协会宣布，由该协会创办的、全国首家电子数据司法鉴定中心——北京网络行业协会电子数据司法鉴定中心正式成立，该中心得到了北京市司法局的司法鉴定资质审核，具备出具法庭承认的电子数据司法鉴定报告的能力。这标志着我市司法部门在处理网络犯罪等案件和纠纷中，有了更加明确和规范的取证手段。

A Novel Deep Learning Method for Application Identification in Wireless Network

In modern wireless communication network, the increased consumer demands for multi-type applications and high quality services have become a prominent trend, and put considerable pressure on the wireless network. In that case, the Quality of Experience(Qo E) has received much attention and has become a key performance measurement for the application and service. In order to meet the users' expectations, the management of the resource is crucial in wireless network, especially the Qo E based resource allocation. One of the effective way for resource allocation management is accurate application identification. In this paper, we propose a novel deep learning based method for application identification. We first analyse the requirement of managing Qo E for wireless communication, and review the limitation of the traditional identification methods. After that, a deep learning based method is proposed for automatically extracting the features and identifying the type of application. The proposed method is evaluated by using the practical wireless traffic data, and the experiments verify the effectiveness of our method.

Modeling of Ship Maneuvering Motion Using Neural Networks

In this paper, Neural Networks （NNs） are used in the modeling of ship maneuvering motion. A nonlinear response model and a linear hydrodynamic model of ship maneuvering motion are also investigated. The maneuverability indices and linear non-dimensional hydrodynamic derivatives in the models are identified by using two-layer feed forward NNs. The stability of parametric estimation is confirmed. Then, the ship maneuvering motion is predicted based on the obtained models. A comparison between the predicted results and the model test results demonstrates the validity of the proposed modeling method.

Identification of Differentially Expressed Genes in Grape Skin at Veraison and Maturity and Construction of Co-expression Network

The ripening process of grape is an important stage during grape growth and development. During this process, color of grape skin is the most obvious change. The molecular mechanism for the ripening of grape(a non-climacteric fruit, which ripens without ethylene and respiration bursts) is still unclear. Although numerous studies have been done on the changes in the contents of metabolites during grape ripening, the differentially expressed genes at veraison and maturity stages have not been systematically analyzed. In this study, 1 524 genes that are significantly differentially expressed in grape(Pinot Noir) skin at veraison and maturity stages were identified, and a co-expression network of these genes was built. Some of the eight co-expression modules we identified may be closely related to the synthesis or metabolism of anthocyanins, sugar acids, and other flavor substances. The transcription factor families WRKY, b ZIP, HSF and WOX may play an important role in the regulation of anthocyanin synthesis or metabolism. The results provide a foundation for further study of the molecular mechanism of grape ripening.

AUTO-IDENTIFICATION OF FOREST FIRE-POINTS IN NOAA IMAGES BASED ON NEURAL NETWORK

Identification of forest fire-points in NOAA images in the basis of monitoring forest fire using NOAA satellite data. Traditional visual interpretation is difficult to settle for auto identification with computer. The artificial neural network technique provides a new means for solving this problem. In this paper, the principles and method of using neural network technique to automatically identify fire-points in NOAA images are discussed and the test in the range of Hubei province is presented. The result of the test shows that the disciplined neural network has collected the character of fire-points and has ability to identify fire-points in NOAA images. Comparing neural network with visual interpretation, the conclusion is drawn that by using neural network the purpose of auto-identification of forest fire-points in NOAA images can be realized with the almost same precision.

Classification and Identification of Nuclear, Biological or Chemical Agents Taken from Remote Sensing Image by Using Neural Network

In the context of new risks and threats associated to nuclear, biological and chemical （NBC） attacks, and given the shortcomings of certain analytical methods such as principal component analysis （PCA）, a neural network approach seems to be more accurate. PCA consists in projecting the spectrum of a gas collected from a remote sensing system in, firstly, a three-dimensional space, then in a two-dimensional one using a model of Multi-Layer Perceptron based neural network. It adopts during the learning process, the back propagation algorithm of the gradient, in which the mean square error output is continuously calculated and compared to the input until it reaches a minimal threshold value. This aims to correct the synaptic weights of the network. So, the Artificial Neural Network （ANN） tends to be more efficient in the classification process. This paper emphasizes the contribution of the ANN method in the spectral data processing, classification and identification and in addition, its fast convergence during the back propagation of the gradient.

Development of Intelligent Garment Coordination and Virtual Try-on System for Fashion Retailing with RFID Technology

Improving customer experience has become a more and more important role in enhancing customer service in fashion retailing business. In this study, a kind of intelligent garment coordination and try-on system for fashion retailing was proposed. Radio Frequency Identification （RFID） technology was used to identify customer and garment item automatically. The intelligent procedure for garment coordination recommendation using Artificial Neural Network （ANN） was developed to imitate fashion designers＇ decision-making on garment coordination. Virtual try-on algorithm based on the customer＇s 2D/mago was accomplished using imagewarping technique. The system architecture and the software framework were also described. The results show that the ＇system is a practical and useful application for fashion retailers.

口岸植物有害生物移动鉴定APP的开发与应用思考

随着我国移动互联网、云计算的技术发展和云存储的基础设施建设,借鉴现有的生物鉴定类APP应用技术,开发和应用"植物有害生物移动鉴定APP"以帮助口岸检疫人员在查验现场完成有害生物鉴定成为了可能。此类APP作为"智能网络检疫鉴定实验室",一旦经过官方认可,将减轻植物检疫实验室90%左右的工作量,昆虫等有害生物鉴定所花费的时间将从平均3～5 d减至15 min以内,将为全面提高检验检疫通关效率,助力检验检疫创新发展做出巨大的贡献。

检察环节电子证据的审查

在检察环节,检察机关对电子证据的审查认定,一要审查电子证据的来源;二要审查电子证据的存储过程;三要审查电子证据的传送通道。在审查方法上,一是利用专门的仪器、设备,通过运行特定程序对电子证据的形成过程进行检查及验证;二是利用技术设备对电子证据所反映内容真伪进行鉴别;三是利用证据间的逻辑关系来判明电子证据的原始性与真实性;四是审查电子证据的收集环节;五是检验电子证据是否被删改过。

Identification of sources of pollution and contamination in water distribution networks based on pattern recognition

An intrusion of contaminants into the water distribution network (WDN) can occur through storage tanks (via animals, dust-carrying bacteria, and infiltration) and pipes. A sensor network could yield useful observations that help identify the location of the source, the strength, the time of occurrence, and the duration of contamination. This paper proposes a methodology for identifying the contamination sources in a water distribution system, which identifies the key characteristics of contamination, such as location, starting time, and injection rates at different time intervals. Based on simplified hypotheses and associated with a high computational efficiency, the methodology is designed to be a simple and easy-to-use tool for water companies to ensure rapid identification of the contamination sources, The proposed methodology identifies the characteristics of pollution sources by matching the dynamic patterns of the simulated and measured concentrations. The application of this methodology to a literature network and a real WDN are illustrated with the aid of an example. The results showed that if contaminants are transported from the sources to the sensors at intervals, then this method can identify the most possible ones from candidate pollution sources. However, if the contamination data is minimal, a greater number of redundant contamination source nodes will be present. Consequently, more data from different sensors obtained through network monitoring are required to effectively use this method for locating multi-sources of contamination in the WDN.

A fine-grained perspective on the robustness of global cargo ship transportation networks

The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transportation network based on the most recent Automatic Identification System（AIS） data available. First, we subdivide three typical cargo ship transportation networks（i.e., oil tanker, container ship and bulk carrier） from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, including random attack and three intentional attacks（i.e., degree-based attack, betweenness-based attack and flux-based attack） to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1） compared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation; 2） different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3） small-scale intentional attacks may have significant influence on the integrity of the container network but a minor impact on the bulk carrier and oil tanker transportation networks. These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation system.Abstract： The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transporta- tion network based on the most recent Automatic Identification System （AIS） data available. First, we subdivide three typical cargo ship transportation networks （i.e., oil tanker, container ship and bulk carrier） from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, in- cluding random attack and three intentional attacks （i.e., degree-based attack, between- ness-based attack and flux-based attack） to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1） com- pared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation 2） different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3） small-scale intentional attacks may have significant influence on the integrity of the con- tainer network but a minor impact on the bulk carrier and oil tanker transportation networks.These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation system.