融合标签关联关系与用户社交关系的微博推荐方法

通过分析微博特点及现有微博推荐算法的缺陷,提出一种融合了标签间关联关系与用户间社交关系的微博推荐方法.采用标签检索策略对未加标签和标签较少的用户进行加标,构建用户-标签矩阵,得到用户标签权重,为了解决该矩阵中稀疏的问题,通过挖掘标签间的关联关系,继而更新用户-标签矩阵.考虑到多用户之间社交关系对挖掘用户兴趣并进行微博推荐的重要性,构建用户-用户社交关系相似度矩阵,并与更新后的用户-标签矩阵进行迭代,得到最终的用户兴趣并进行相关推荐.实验证明了该算法针对微博信息推荐是有效的.     

Cross-modal social image clustering and tag cleansing

In this paper, a cross-modal approach is developed for social image clustering and tag cleansing. First, a semantic image clustering algorithm is developed for assigning large-scale weakly-tagged social images into a large number of image topics of interest. Spam tags are detected automatically via sentiment analysis and multiple synonymous tags are merged as one super-topic according to their inter-topic semantic similarity contexts. Second, multiple base kernels are seamlessly combined by maximizing the correlations between the visual similarity contexts and the semantic similarity context, which can achieve more precise characterization of cross-modal (semantic and visual) similarity contexts among weakly-tagged social images. Finally, a K-way min–max cut algorithm is developed for social image clustering by minimizing the cumulative inter-cluster cross-modal similarity contexts while maximizing the cumulative intra-cluster cross-modal similarity contexts. The optimal weights for base kernel combination are simultaneously determined by minimizing the cumulative within-cluster variances. The polysemous tags and their ambiguous images are further split into multiple sub-topics for reducing their within-topic visual diversity. Our experiments on large-scale weakly-tagged Flickr images have provided very positive results.     

基于核心标签的可重叠微博网络社区划分方法

针对传统微博社区发现算法内聚低重叠度不可控制等问题,以自顶向下的策略,提出一种基于核心标签的可重叠微博社区发现策略Tag Cut.先利用用户标签的共现关系及逆用户频率对标签进行加权,并基于标签之间的内联及外联关系并将用户的标签进行扩充,然后在整体社区中提取包含某一标签的用户作为临时分组并利用评价函数评估划分的优劣,最后选出最合适的核心标签根据其对应分组与其他分组距离的远近来决定将其划分为新的分组还是并入其他分组.用此策略反复迭代直到满足要求.该算法划分的组由若干个拥有核心标签的分组组成且综合利用微博用户已声明的及隐含的兴趣、用户之间的关注规律、结果的实用性对划分结果进行修正.经真实数据实验表明该方法内聚高社区重叠度可控且拥有实际意义.     

融合标签平均划分距离和结构关系的微博用户可重叠社区发现

提出了一种融合标签平均划分距离和结构关系的微博用户可重叠社区发现算法.首先从信息论与距离的概念出发,定义基于核心标签平均划分距离的准划分算法;再根据用户关注关系定义结构属性向量,并计算用户结构相异度,进而对核心标签平均划分距离和用户结构相异度进行权重调节,得到综合划分相异度;最后将综合划分相异度最低的标签所划分出的分组作为本次循环的新社区;实验表明,该方法能够识别可重叠社区且具有实际应用意义.     

一种基于社会化标签的协同过滤推荐算法

为了减少社会化标签的语义模糊和冗余给基于标签的协同过滤算法带来的噪声,利用群体智慧选择流行标签对用户和资源建模,在此基础上设计了基于流行标签的协同过滤算法。实验证明,该算法降低了标签噪声,并提高了传统的基于标签协同过滤算法的准确性。     

基于主题模型的博客标签语义知识获取

This paper focuses on semantic knowledge acquisition from blogs with the proposed tag-topic model. The model extends the Latent Dirichlet Allocation (LDA) model by adding a tag layer between the document and the topic. Each document is represented by a mixture of tags; each tag is associated with a multinomial distribution over topics and each topic is associated with a multinomial distribution over words. After parameter estimation, the tags are used to describe the underlying topics. Thus the latent semantic knowledge within the topics could be represented explicitly. The tags are treated as concepts, and the top-N words from the top topics are selected as related words of the concepts. Then PMI-IR is employed to compute the relatedness between each tag-word pair and noisy words with low correlation removed to improve the quality of the semantic knowledge. Experiment results show that the proposed method can effectively capture semantic knowledge, especially the polyseme and synonym.     

基于模运算标签分类的RFID标签防碰撞识别方法

为了解决射频识别(RFID)系统中的多标签防碰撞问题,在分析帧时隙ALOHA算法的基础上,提出一种基于模运算标签分类的RFID标签防碰撞识别方法。引入一种检测信息碰撞的时隙选择信息,对标签所选取时隙的碰撞情况进行分析并估计标签数量;然后对标签EPC编码进行逐级的取模运算,将同余的标签归为一组。各个标签经过K次取模运算后,分为2k组,每组只有发生少量碰撞位的标签。再将标签按照分组对应的时隙发送,碰撞标签采用二叉树后退式算法处理。本方法极大的提高了标签的识别效率,适用于射频识别系统中阅读器对于大量电子标签的快速识别。     

Web image concept annotation with better understanding of tags and visual features

This paper focuses on improving the semi-manual method for web image concept annotation. By sufficiently studying the characteristics of tag and visual feature, we propose the Grouping-Based-Precision & Recall-Aided (GBPRA) feature selection strategy for concept annotation. Specifically, for visual features, we construct a more robust middle level feature by concatenating the k-NN results for each type of visual feature. For tag, we construct a concept-tag co-occurrence matrix, based on which the probability of an image belonging to certain concept can be calculated. By understanding the tags’ quality and groupings’ semantic depth, we propose a grouping based feature selection method; by studying the tags’ distribution, we adopt Precision and Recall as a complementary indicator for feature selection. In this way, the advantages of both tags and visual features are boosted. Experimental results show our method can achieve very high Average Precision, which greatly facilitates the annotation of large-scale web image dataset.     

Automatic Abstract Tag Detection for Social Image Tag Refinement and Enrichment

Collaborative image tagging systems, such as Flickr, are very attractive for supporting keyword-based image retrieval, but some user-provided tags of collaboratively-tagged social images might be imprecise. Some people may use general or high-level words (i.e., abstract tags) to tag their images for saving time and effort, but such general or high-level tags are too abstract to describe the visual content of social images precisely. As a result, users may not be able to find what they need when they use the specific keywords for query specification. To tackle the problem of abstract tags, an ontology with three-level semantics is constructed for detecting the candidates of abstract tags from large-scale social images. Then the image context (nearest neighbors) and tag context (most relevant tags) of social images with abstract tags are used to ultimately confirm whether these candidates are abstract or not and identify the specific tags which can further depict the images with abstract tags. In addition, all the relevant tags, which correspond with intermediate nodes between the abstract tags and specific tags on our concept ontology, are added to enrich the tags of social images so that users can have more choices to select various keywords for query specification. We have tested our proposed algorithms on two types of data sets (revised standard datasets and self-constructed dataset) and compared our approach with other approaches.     

异质社交网络中多通道特征融合的好友推荐模型

合理有效的好友推荐算法对于社交网络的发展和扩张有重大的意义。然而随着社交网络的复杂化和异质化,传统推荐系统中协同过滤推荐方法不能满足需求。针对异质社交网络中存在着大量的内容相关信息这一特点,根据好友推荐的需求,提出了多通道特征融合的好友推荐模型。该模型对用户相关的多维特征进行挖掘与利用,包括显性特征(如用户profile,用户tag,社交关系等)和隐性特征(如用户重要度,挖掘用户标注发现其领域兴趣等),并进一步将这些内容相关的多特征融合到协同排序算法中进行学习训练。实验结果表明,随着多个内容特征的逐步融合,算法的MAP值稳步提高,最终相对未融合的协同排序方法提高了12%,并在一定程度上的解决了冷启动问题,提高了好友推荐的多样性。     

标签分布疏密状态变化情形下超高频天线设计方法研究

针对标签分布疏密程度的变化会导致其天线与负载的阻抗匹配关系改变进而影响系统性能的问题。该文结合电磁波传播理论和射频识别(RFID)工作原理,导出了标签分别处于稀疏和密集分布状态下的RFID系统通信链路模型;结合变压器模型和二端口网络分析方法,推导了标签密集分布状态时标签天线的互阻抗表达式;利用功率传输系数和反向散射调制因子,分析了标签分布疏密程度对RFID系统性能的影响;基于加载条匹配原理,提出一种适用于分布疏密状态变化情形的标签天线优化设计方法。仿真实验和实际测量结果表明,标签密集分布时,改进标签的性能较原型标签提升16%;标签稀疏分布时,改进标签的性能达到原型标签的96%。     

Minimising collisions in RFID data streams using probabilistic Cluster-Based Technique

Radio Frequency Identification (RFID) uses wireless radio frequency technology to automatically identify tagged objects. Despite the extensive development of the RFID technology in many areas, tags collisions still remain a major problem. This issue is known as the collision problem and can be solved by using anti-collision techniques. Current probabilistic anti-collision approaches suffer from tag starvation due to the inaccurate Backlog estimation and have a low performance in some cases. In this research, we propose a Probabilistic Cluster-Based Technique (PCT) to maximise the performance efficiency during the tag identification process. The PCT approach creates new tag grouping strategies using particular equations, according to the optimal efficiency obtained for a specific number of tags. Through extensive experimentation, we have demonstrated that the proposed concept performs better than the other current state-of-the-art approaches.     

可并行识别的分组动态帧时隙ALOHA标签防碰撞算法

该文针对现有动态帧时隙ALOHA标签防碰撞算法的系统吞吐率低、算法效率低等问题,提出一种可并行识别的分组动态帧时隙ALOHA(PIGDFSA)标签防碰撞算法。该文以实验为基础,探索了待识别标签数、标签分组数、帧长对系统吞吐率与标签碰撞率的影响,研究了提升系统吞吐率与降低标签碰撞率的策略与方法。结合射频识别(RFID)的多天线系统,引入FastICA技术,从而实现碰撞时隙重新定义,并以此为基础,利用未识别标签数目自适应确定分组数与帧长。仿真结果表明：PIGDFSA算法在标签数达到2000时,算法吞吐率仍能稳定在92%以上,与FSA-256, GDFSA, BSDBG等算法相比具有更高的算法吞吐率,更少的空隙时隙,更高的算法效率。     

Hybrid textual-visual relevance learning for content-based image retrieval

Learning effective relevance measures plays a crucial role in improving the performance of content-based image retrieval (CBIR) systems. Despite extensive research efforts for decades, how to discover and incorporate semantic information of images still poses a formidable challenge to real-world CBIR systems. In this paper, we propose a novel hybrid textual-visual relevance learning method, which mines textual relevance from image tags and combines textual relevance and visual relevance for CBIR. To alleviate the sparsity and unreliability of tags, we first perform tag completion to fill the missing tags as well as correct noisy tags of images. Then, we capture users’ semantic cognition to images by representing each image as a probability distribution over the permutations of tags. Finally, instead of early fusion, a ranking aggregation strategy is adopted to sew up textual relevance and visual relevance seamlessly. Extensive experiments on two benchmark datasets well verified the promise of our approach.     

融合链接拓扑结构和用户兴趣的朋友推荐方法

提出一种新的朋友推荐方法,该方法同时使用用户兴趣和朋友关系这2种因素来为目标用户推荐朋友,对PageRank算法进行改进,提出一种能同时融合上述2种因素的Topic_Friend_PageRank(TFPR)模型。首先,采用LDA(latent Dirichlet allocation)分析用户发布的消息内容,将用户表示为若干主题上的分布,从而建模用户的兴趣。接下来,使用加权的PageRank算法建模用户在整个链接拓扑中的重要程度和用户之间朋友关系的相似性。最后根据主题感知的PageRank思想,将用户兴趣融入前面提到的加权PageRank中,形成同时融合用户兴趣和朋友关系的TFPR模型。采用新浪微博数据验证所提模型的性能,实验证明该模型能同时得到较高的准确率和召回率。     

Protecting Your Privacy with a Mobile Agent Device in RFID Environment

Radio Frequency Identification (RFID) Technology is a contactless automatic identification technology using radio frequency. For this RFID technology to be widely spread, the problem of privacy invasion should be solved. There are many research works in progress to solve the RFID privacy problems. Most of works for solving this problem have focused on developing light-weight cryptographic modules which can be embedded into RFID tags, but some of them used a proxy agent approach that control communications between the tag and the reader for protecting user privacy. The later approach is very useful and practical in terms of manufacturing low-cost tag hardware. However, all schemes of this approach have some problems in ownership transfer and forgery detection. In this paper, we are focusing on the proxy agent approach and we suggest an advanced agent scheme that guarantees not only privacy protection but also forgery detection. And our scheme is more scalable than other agent schemes so far.     

Graph regularized low-rank feature mapping for multi-label learning with application to image annotation

Automatic image annotation has emerged as a hot research topic in the last two decades due to its application in social images organization. Most studies treat image annotation as a typical multi-label classification problem, where the shortcoming of this approach lies in that in order to a learn reliable model for label prediction, it requires sufficient number of training images with accurate annotations. Being aware of this, we develop a novel graph regularized low-rank feature mapping for image annotation under semi-supervised multi-label learning framework. Specifically, the proposed method concatenate the prediction models for different tags into a matrix, and introduces the matrix trace norm to capture the correlations among different labels and control the model complexity. In addition, by using graph Laplacian regularization as a smooth operator, the proposed approach can explicitly take into account the local geometric structure on both labeled and unlabeled images. Moreover, considering the tags of labeled images tend to be missing or noisy, we introduce a supplementary ideal label matrix to automatically fill in the missing tags as well as correct noisy tags for given training images. Extensive experiments conducted on five different multi-label image datasets demonstrate the effectiveness of the proposed approach.     

A low-cost wireless sensors positioning solution for indoor parking facilities management

A relatively low-cost system for indoor parking facilities management is proposed, which is a combined solution of RFID/WiFi and a MEMS IMU monitoring scheme. An RFID localisation module is proposed in the form of so-called virtual gates. To define such virtual gates, either RFID tags or readers are placed at known locations throughout the area of interest. In this study, a number of tags are fixed at known positions and a moving reader is carried by each participating vehicle. Based on this configuration set-up, the Cell of Origin (CoO) technique is applied, in which the system indicates the presence of the user carrying the reader in a cell around the tag location. To define a virtual gate, tags are installed along the parking lot corridors and at critical transit passages in the parking facility. The CoO technique is also proposed in the case of WiFi for location determination of vehicles in a multi-storey car park. In this study, WiFi is employed to monitor the passing vehicles and bridge the gap until a tag can detect a user’s reader again. Thus, a combined positioning solution of RFID and WiFi is achieved. As a complement to the proposed RFID/WiFi system, this study examines the potential and limitations of MEMS IMU sensors (i.e. accelerometers, gyroscopes and barometers) commonly found in modern smartphones. The paper concludes with a detailed discussion on the implications of alternative positioning techniques for indoor parking management.     

融合多权重因素的低秩概率矩阵分解推荐模型

针对个性化推荐精度较低、对冷启动敏感等问题,该文提出一种融合多权重因素的低秩概率矩阵分解推荐模型MWFPMF。模型利用给定的社交网络构建信任网络,借助Page rank算法和信任传递机制求取用户间信任度;基于Page rank计算用户社会地位,利用活动评分和评分时间修正用户间关系权重;引入词频-逆文本频率技术(TF-IDF)求取用户标签,通过标签相似性表征用户间同质性;将用户间信任度、用户社会地位影响力和用户同质性3因素融入低秩概率矩阵分解中,从而使用户偏好和活动特征映射到同一低秩空间,实现用户-活动评分矩阵的分解,在正则化约束下,最终完成低秩特征矩阵对用户评分缺失的有效预测。利用豆瓣同城北京和Ciao数据集确定各模块的参数设置值。通过仿真对比实验可知,本推荐模型获得了较高的推荐精度,与其他5种传统推荐算法相比,平均绝对误差至少降低了6.58%,均方差误差至少降低了6.27%,与深度学习推进算法相比,推荐精度基本接近;在冷启动用户推荐上优势明显,与其他推荐算法相比,平均绝对误差至少降低了0.89%,均方差误差至少降低了3.01%。