DEAON： dynamically-evolving active overlay networks for scalable information retrieval

In this paper, we propose the dynamically-evolving active overlay network （DEAON）, which is an efficient, scalable yet simple protocol to facilitate applications of decentralized information retrieval in P2P networks. DEAON consists of three novel components ： a Desirable Topology Construction and Adaptation algorithm to guide the evolution of the overlay topology towards a small-world-like graph; a Semantic-based Neighbor Selection scheme to conduct an online neighbor ranking; a Topology-aware Intelligent Search mechanism to forward incoming queries to deliberately selected neighbors. We deploy and compare DEAON with other several existing distributed search techniques over static and dynamic environments. The results indicate that DEAON outperforms its competitors by achieving higher recall rate while using much less network resources, in both of the above environments.

Hybrid Chinese Information Retrieval Model Based on the Combination of Keyword and Concept

A hybrid model that is based on the Combination of keywords and concept was put forward. The hybrid model is built on vector space model and probabilistic reasoning network. It not only can exert the advantages of keywords retrieval and concept retrieval but also can compensate for their shortcomings. Their parameters can be adjusted according to different usage in order to accept the best information retrieval result, and it has been proved by our experiments.

A High-Speed Information Retrieval System

We cleveloped a high-speed information retrieval system. The system hased on the IXP 2800 is one of the dedicute device. The velocity of the information retrieval is 6.8 Gb/s. The protocol support Telnet, FTP, SMTP, POP3 etc. various networks protocols. The information retrieval supports the key word and the natural language process. This paper explains the hardware system, software system and the index of the performance. Key words network processor - IXP2800 - information retrieval - IXA CLC number TP 309 Foundation item: Supported by the National Natural Science Foundation of China (69873016 & 69972017) and the National High Technology Development Program of China (863-301-06-1)Biography: SHI Shu-dong (1963-), male, Ph. D. candidate, research direction: network & information security.

Dominant Meaning Method for Intelligent Topic-Based Information Agent towards More Flexible MOOCs

The use of agent technology in a dynamic environment is rapidly growing as one of the powerful technologies and the need to provide the benefits of the Intelligent Information Agent technique to massive open online courses, is very important from various aspects including the rapid growing of MOOCs environments, and the focusing more on static information than on updated information. One of the main problems in such environment is updating the information to the needs of the student who interacts at each moment. Using such technology can ensure more flexible information, lower waste time and hence higher earnings in learning. This paper presents Intelligent Topic-Based Information Agent to offer an updated knowledge including various types of resource for students. Using dominant meaning method, the agent searches the Internet, controls the metadata coming from the Internet, filters and shows them into a categorized content lists. There are two experiments conducted on the Intelligent Topic-Based Information Agent: one measures the improvement in the retrieval effectiveness and the other measures the impact of the agent on the learning. The experiment results indicate that our methodology to expand the query yields a considerable improvement in the retrieval effectiveness in all categories of Google Web Search API. On the other hand, there is a positive impact on the performance of learning session.

Lung Nodule Image Retrieval Based on Convolutional Neural Networks and Hashing

Lung medical image retrieval based on content similarity plays an important role in computer-aided diagnosis of lung cancer.In recent years,binary hashing has become a hot topic in this field due to its compressed storage and fast query speed.Traditional hashing methods often rely on highdimensional features based hand-crafted methods,which might not be optimally compatible with lung nodule images.Also,different hashing bits contribute to the image retrieval differently,and therefore treating the hashing bits equally affects the retrieval accuracy.Hence,an image retrieval method of lung nodule images is proposed with the basis on convolutional neural networks and hashing.First,apre-trained and fine-tuned convolutional neural network is employed to learn multilevel semantic features of the lung nodules.Principal components analysis is utilized to remove redundant information and preserve informative semantic features of the lung nodules.Second,the proposed method relies on nine sign labels of lung nodules for the training set,and the semantic feature is combined to construct hashing functions.Finally,returned lung nodule images can be easily ranked with the query-adaptive search method based on weighted Hamming distance.Extensive experiments and evaluations on the dataset demonstrate that the proposed method can significantly improve the expression ability of lung nodule images,which further validates the effectiveness of the proposed method.

PIR-based data integrity verification method in sensor network

Since a sensor node handles wireless communication in data transmission and reception and is installed in poor environment, it is easily exposed to certain attacks such as data transformation and sniffing. Therefore, it is necessary to verify data integrity to properly respond to an adversary's ill-intentioned data modification. In sensor network environment, the data integrity verification method verifies the final data only, requesting multiple communications. An energy-efficient private information retrieval(PIR)-based data integrity verification method is proposed. Because the proposed method verifies the integrity of data between parent and child nodes, it is more efficient than the existing method which verifies data integrity after receiving data from the entire network or in a cluster. Since the number of messages for verification is reduced, in addition, energy could be used more efficiently. Lastly, the excellence of the proposed method is verified through performance evaluation.

Deep Neural Network and Pseudo Relevance Feedback Based Query Expansion

The neural network has attracted researchers immensely in the last couple of years due to its wide applications in various areas such as Data mining,Natural language processing,Image processing,and Information retrieval etc.Word embedding has been applied by many researchers for Information retrieval tasks.In this paper word embedding-based skip-gram model has been developed for the query expansion task.Vocabulary terms are obtained from the top“k”initially retrieved documents using the Pseudo relevance feedback model and then they are trained using the skip-gram model to find the expansion terms for the user query.The performance of the model based on mean average precision is 0.3176.The proposed model compares with other existing models.An improvement of 6.61%,6.93%,and 9.07%on MAP value is observed compare to the Original query,BM25 model,and query expansion with the Chi-Square model respectively.The proposed model also retrieves 84,25,and 81 additional relevant documents compare to the original query,query expansion with Chi-Square model,and BM25 model respectively and thus improves the recall value also.The per query analysis reveals that the proposed model performs well in 30,36,and 30 queries compare to the original query,query expansion with Chi-square model,and BM25 model respectively.

Challenges and Possibilities for the Household Medicine Lease （HML） System Viewed in Light of CRM： HML-Based Everyday Self-Medication in Ubiquitous Network Society

Household medicine lease （HML） industry originated way back in the Edo period （17C-19C）, when it was promoted by the local fiefdom government to revitalize the economy. Accumulations of wealth, acquired thereafter from everywhere outside the region, have culminated in the formation of the present-day industrial cluster in Toyama, the largest in the whole area facing the Sea of Japan. Today an adaptation of the quasi-CRM （customer relationship management） business model of the HML system has proved to be a success in Mongolia. This fact seems to offer the authors some clues for dealing with those problems that healthcare and medical services in Japan and elsewhere are riddled with. In this paper, focusing on the common critical success factors （CSFs） behind the success of the authors＇ prototype HML system and its recent successful application in Mongolia, the authors will analyze these factors from the perspective of CRM. The authors will then clarify the following： （1） the usefulness of the business model for ensuring primary healthcare for people in developing countries; （2） the usefulness in our ubiquitous network society of applying ICT to the HML system; （3） possible contributions that the use of the system can make toward improving the quality of our everyday healthcare and medical services in our prominently aging society; and the authors will also suggest （4） the importance of elevating ＂individual self-medication＂ to ＂community-based self-medication＂.

发展“四全媒体”助力北京“四个中心”建设:问题与关键技术

新型媒体建设应该成为北京“四个中心”城市战略定位的重要助力。本文首先分析了“四个中心”建设对媒体的要求和发展“四全媒体”目前存在的技术和机制问题,然后通过示例探讨了发展“四全媒体”的技术可行性。实验结果表明“全程媒体”可以基于ICN网络的技术架构,基于写作风格的虚假信息识别技术和多语言新闻监测可以作为实现“全息媒体”的重要手段,“全效媒体”的媒体资产共享问题的解决可以基于区块链技术,分析表明分形理论、博弈论可能会成为“全员媒体”组织方式和机制制定的理论基础,最后提出了北京应自觉发展“四全媒体”的建议。

体验式教学融入线上线下结合的“大学生心理健康教育”课程的改革探索

本文介绍了体验式教学融入线上线下结合的“大学生心理健康教育”课程的课程准备、教学实施、效果评价、反思展望等情况,证实了“体验式教学+线上线下结合”的教学模式能够推动学生自主学习,实现“知、情、意、行”四个方面的教学目标,是顺应时代需求、符合心理发展规律的教学改革探索。

基于图卷积的无监督跨模态哈希检索算法

为解决当前无监督跨模态哈希检索在全局相似性矩阵构建和异构数据语义信息融合中存在的困难,提出一种基于图卷积的无监督跨模态哈希检索算法(GCUH)。采用分层次聚合的方式,将各个模态的相似性结构编码到全局相似性矩阵中,获得跨模态的成对相似性信息来指导学习。使用图卷积模块融合跨模态信息,消除邻居结构中的噪声干扰,形成完备的跨模态表征,提出两种相似性保持的损失函数约束哈希码的一致性。与基线模型相比,GCUH在NUS-WIDE数据集上使用64位哈希码执行文本检索图片任务的检索精度提升了6.3%。

信息检索课程快速转型实作

高校图书馆长期承担信息检索课程,已经具备一定的教学资源和知识储备,新的信息环境下,面临如何借助原有资源快速实现课程转型的挑战。本文分析信息检索课程现状,尝试调整、重组教学内容,使用多种教学辅助方式促进教学效果,为将来可能的变化留下发展和规划空间。

图书馆网络信息检索方法与技巧

随着互联网技术的飞速发展,图书馆信息服务模式正经历着从传统的纸质资料查阅向数字化、网络化方向转变。网络信息检索已成为图书馆服务的重要组成部分,为用户提供高效、便捷的知识获取途径。该文旨在探讨图书馆网络信息检索的方法与技巧,包括搜索引擎的优化使用、数据库资源的高效挖掘、信息筛选与评估策略,以及个性化信息服务的实现,旨在提升用户的信息检索便利度,促进学术研究与服务学习型社会建设。

基于学习通平台的课程知识图谱构建及其应用——以《信息检索》课程为例

[目的/意义]知识图谱理论和技术在课程知识图谱和学科知识图谱等领域已经得到了广泛应用。本研究旨在探讨如何基于学习通平台构建《信息检索》课程的知识图谱,并分析其在教学实践中的应用。[方法/过程]首先明确课程知识图谱的定义,并阐述课程知识图谱在教育学领域的重要性。其次,详细描述了课程知识图谱的构建过程,包括知识点的梳理、知识点之间逻辑关系的建立、以及教学资源与知识点的关联。探讨了如何利用知识图谱进行学情分析。[结果/结论]研究表明,基于学习通平台构建的课程知识图谱能够有效地支持教师的教学活动和学生的学习过程。知识图谱不仅能够帮助教师精准地形成课程知识体系,明确知识类型与认知维度,还能为学生提供个性化的学习路径,促进深度学习和综合能力的提升。此外,知识图谱的应用还有助于教师实时了解学生的学习情况,实现精准教学。

网络信息技术赋能思政课教学的依据与创新

近年来,网络信息技术赋能高校思政课,契合了思政课教学模式改革的内在诉求,由此衍生出以微电影、短视频为手段的特色教学和以智慧教学软件为工具的智慧教学等新型教学模式,摆脱了传统教学模式的枯燥刻板,有效实现了课内课外、线上线下的结合,显著提升了思政课教学的活跃度和实效性,但同时也陷入了教育资源使用不均、教学模式难以推广、教育资源大量闲置、“新瓶装旧酒”现象频发等现实困局。只有推进思政课教学智能化基础设施建设、数字思政课教学新模式基地建设、网络信息资源平台建设、思政课教师队伍建设等,才能走出网络信息技术赋能思政课教学的现实困局。

舰船通信关联信息目标数据检索方法研究

舰船通信系统会产生大量的通信关联信息,难以在海量的通信关联信息中,捕获舰船通信数据的关键属性,对此,提出舰船通信关联信息目标数据检索方法。通过计算舰船通信关联信息全局检索的特征量,对舰船网络数据进行聚类和信息融合,结合深度卷积神经网络和哈希学习算法构建目标数据检索模型,得到目标数据的初步检索结果。使用汉明距离计算初步检索结果的相似度,比较目标检索数据与初步检索结果的哈希码之间的汉明距离,输出与舰船通信关联信息目标数据相似度最高的检索结果,实现目标数据检索。实验验证,该方法实现的目标数据检索MAP值能够达到98%以上,实现的不同信息种类数据检索的时间消耗均能保证在在8 ms以下,能够为相关人员提供更加便捷和可靠的信息服务,为舰船管理、维护和作战决策提供有力的支持。

A Weighted Multi-Layer Analytics Based Model for Emoji Recommendation

The developed system for eye and face detection using Convolutional Neural Networks(CNN)models,followed by eye classification and voice-based assistance,has shown promising potential in enhancing accessibility for individuals with visual impairments.The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system.This research significantly contributes to the field of accessibility technology by integrating computer vision,natural language processing,and voice technologies.By leveraging these advancements,the developed system offers a practical and efficient solution for assisting blind individuals.The modular design ensures flexibility,scalability,and ease of integration with existing assistive technologies.However,it is important to acknowledge that further research and improvements are necessary to enhance the system’s accuracy and usability.Fine-tuning the CNN models and expanding the training dataset can improve eye and face detection as well as eye classification capabilities.Additionally,incorporating real-time responses through sophisticated natural language understanding techniques and expanding the knowledge base of ChatGPT can enhance the system’s ability to provide comprehensive and accurate responses.Overall,this research paves the way for the development of more advanced and robust systems for assisting visually impaired individuals.By leveraging cutting-edge technologies and integrating them into amodular framework,this research contributes to creating a more inclusive and accessible society for individuals with visual impairments.Future work can focus on refining the system,addressing its limitations,and conducting user studies to evaluate its effectiveness and impact in real-world scenarios.

A Novel Supercapacitor Degradation Prediction Using a 1D Convolutional Neural Network and Improved Informer Model

Safety and reliability are crucial for the next-generation supercapacitors used in energy storage systems,while accurate prediction of the degradation trajectory and remaining useful life(RUL)is essential for analyzing degradation and evaluating performance in energy storage systems.This study proposes a novel data processing and improved one-dimensional convolutional neural network(1D CNN)-informer framework for robust RUL prediction.In data preprocessing,all data from two structures are adjusted to a unified format,and cross-entropy loss is used to couple the 1D CNN and informer.Then,the minimum-maximum feature scaling method is used for normalization to accelerate the training process in reaching the minimum cost function.A relative position encoding algorithm is introduced to improve the Informer model,enabling it to better learn the sequence relationships between data and effectively reduce prediction variability.Supercapacitor data in dif-ferent working conditions are used to validate the proposed method.Compared with other existing methods,the maximum root mean square error is reduced by 32.71%,the mean absolute error is reduced by 28.50%,and R^(2) is increased by 4.79%.The strategy considers the complementarity between two single models,which can extract features and enrich local details,as well as enhance the model’s global perception ability.The experimental results demonstrate that the proposed model achieves high-precision and robust RUL prediction,thereby pro-moting the industrial application of supercapacitors.

线上线下混合式教学方法研究与实践——以“信息检索”课程为例

随着科技的发展和互联网的普及,教育方式也在不断地发生变革。线上线下混合式教学方法作为一种新型的教育模式,已经在全球范围内得到了广泛的关注和实践。这种教学方法为后疫情环境和“互联网+”环境下的创新型教学模式,集成了传统课堂和网络课堂的优点,将传统的面对面教学与线上教学相结合,对提高教学质量、学生课堂体验、学生参与度、教学效果均有帮助。文章从底部环境层、运行管理层和课程应用层搭建高校“信息检索”翻转课程系统,以期通过该混合课程系统的建设与实践进一步提高“信息检索”课的教学效果,促进“信息检索”课的持续改革创新。

基于学生信息获取行为规律的信息 检索课目标达成度提升策略研究

[目的/意义]信息检索课承担着支撑学生毕业论文写作和各类创新创业大赛的任务。然而,信息检索课目标达成度并没有得到阶段性的持续提升,这影响着其对其他环节的支撑。由此,探索高校如何基于学生信息行为获取规律提升信息检索课程目标达成度成为需要解决的紧迫问题。[方法/过程]本研究以学生信息获取行为规律出发,结合新型技术对信息检索课程的影响,以经济管理专业为例,分析信息检索课目标达成度形成机理,重点关注信息检索课目标达成度提升策略。[结果/结论]研究发现:信息检索课目标达成度取决于学生信息获取行为规律和信息获取满意度。由于信息检索课目标达成度并未紧跟数字时代变化做出调整,使得知识点与专业毕业达成目标匹配性不强。同时,信息检索课需要紧跟时代发展,将新型技术纳入课程教学之中,以便适应不断变化的人才培养要求。