Pattern discovery and change detection of online music query streams

Mining of music data is one of the most important problems in multimedia data mining. In this paper, two research issues of mining music data, i.e., online mining of music query streams and change detection of music query streams, are discussed. First, we proposed an efficient online algorithm, FTP-stream (Frequent Temporal Pattern mining of streams), to mine all frequent melody structures over sliding windows of music melody sequence streams. An effective bit-sequence representation is used in the proposed algorithm to reduce the time and memory needed to slide the windows. An effective list structure is developed in the FTP-stream algorithm to overcome the performance bottleneck of 2-candidate generation. Experiments show that the proposed algorithm FTP-stream only needs a half of memory requirement of original melody sequence data, and just scans the music query stream once. After mining frequent melody structures, we developed a simple online algorithm, MQS-change (changes of Music Query Streams), to detect the changes of frequent melody structures in current user-centered music query streams. Two music melody structures (set of chord-sets and string of chord-sets) are maintained and four melody structure changes (positive burst, negative burst, increasing change and decreasing change) are monitored in a new summary data structure, MSC-list (a list of Music Structure Changes). Experiments show that the MQS-change algorithm is an effective online method to detect the changes of music melody structures over continuous music query streams.

Stance detection and mobile app recommendation discourse on tweets

Web‐based social networking such as microblogging administrations and long‐range informal communication locales are changing the way in which individuals collaborate on the web and search for data and opinions. An essential parameter of online networking discourse is searchability. A key semiotic asset supporting this capacity is the hashtag, a type of social label that enables microbloggers to insert metadata in online networking posts. In this paper, an attempt is made to analyze stance detection and app recommendation discourse on tweets in view of hashtag techniques, which is in the territory of etymology, and to spotlight the structure of dialect at the provision level. With a revival of enthusiasm for topics identified by modeling language at the discourse level, a graphical model of conversational structure (ie, the structural topic model) has been constructed by means of utilizing three methods: displaying words connected with topics or documents highly connected with topics, calculating topic correlations, and assessing associations between metadata and topical content, its capture of latent topics, and topical structures inside documents on a benchmark dataset (ie, SemEval 2016) has been scrutinized for stance detection, and data have been crawled from Twitter, using the hashtag #App for app recommendations.     

基于心率变异性分析的睡眠音乐推荐系统

现有睡眠监测研究主要是针对睡眠质量提出非干扰式监测方法的研究,而缺乏对睡眠质量主动调节方法的研究。基于心率变异性(HRV)分析的精神状态以及睡眠分期研究主要集中在这两种信息的获取上,而这两种信息的获取需要佩戴专业医疗设备,并且这些研究缺乏对信息的应用以及调整。音乐可以作为一种解决睡眠问题的非药物类方法,但现有音乐推荐方法并未考虑个体睡眠及精神状态的差异。针对以上问题提出了一种基于移动设备的精神压力和睡眠状态的音乐推荐系统。首先,用手表采集光体积扫描计信号来提取特征并计算心率;其次,将采集的信号通过蓝牙传递给手机,手机通过这些信号评估人的精神压力以及睡眠状态来播放调整音乐;最后,根据个体每晚的入眠时间进行音乐推荐。实验结果表明,在使用睡眠音乐推荐系统后,用户睡眠总时长相较于使用前增长11.0%。     

基于内容的推荐与协同过滤融合的新闻推荐方法

针对基于内容的新闻推荐方法中用户兴趣多样性的缺乏问题和混合推荐方法存在的冷启动问题,提出一种基于内容与协同过滤融合的方法进行新闻推荐。首先利用基于内容的方法发现用户既有兴趣;再用内容与行为的混合相似度模式,寻找目标用户的相似用户群,预测用户对特征词的兴趣度,发现用户潜在兴趣;然后将用户既有兴趣与潜在兴趣融合,得到兼具个性化和多样性的用户兴趣模型;最后将候选新闻与融合模型进行相似度计算,形成推荐列表。实验结果显示,与基于内容的推荐方法相比,所提方法的F-measure和整体多样性Diversity均有明显提高;与混合推荐方法相比,性能相当,但候选新闻无需耗时积累足够的用户点击量,不存在冷启动问题。     

Finding maximum-length repeating patterns in music databases

This paper introduces the problem of discovering maximum-length repeating patterns in music objects. A novel algorithm is presented for the extraction of this kind of patterns from a melody music object. The proposed algorithm discovers all maximum-length repeating patterns using an “aggressive” accession during searching, by avoiding costly repetition frequency calculation and by examining as few as possible repeating patterns in order to reach the maximum-length repeating pattern(s). Detailed experimental results illustrate the significant performance gains due to the proposed algorithm, compared to an existing baseline algorithm.

移动环境下基于情境感知的个性化影视推荐算法研究

针对在移动环境下使用传统推荐算法进行个性化影视推荐时存在的准确度不高的问题,提出了一种基于情境感知的矩阵分解算法。该算法在基本矩阵分解算法的基础上,通过融入全局偏置和情境偏置来进行未知评分预测。该算法的优势在于：一方面,使用矩阵分解的方式使得矩阵的规模远远小于原始评分矩阵;另一方面,该算法充分融入了情境要素对评分的影响,使得预测评分更加精准。通过在LDOS-CoMoDa数据集上进行实验,结果表明,该算法在准确度上优于基于用户的协同过滤算法、基本矩阵分解算法和baseline预测算法。     

基于关键词权重量化方法的服务发现技术研究

对隐式经验核心概念及性质进行了形式化的定义与分析,提出了一个新的服务发现模型——ICSSD模型,该模型解决了扩展发布机制、基于语义本体及扩充服务规约结构的方法所不能解决的问题。在服务的行为和QoS参数随时间的变化而不断变化的过程中,服务的功能与非功能特性的表现和控制问题得到一种可行的解决方案。SRService服务推荐系统的开发实践表明,ICSSD模型有效提高了服务发现的效率,同时也为Web服务的发现过程提供了有效的动态管理机制。     

基于亲和力与研究方向覆盖率的审稿人推荐算法

针对现有审稿人推荐算法主要通过亲和力分数分配审稿人,而忽略了审稿人与论文研究方向匹配的问题,提出一种基于亲和力与研究方向覆盖率的审稿人推荐算法（ARDC）。首先,根据研究方向在待审论文和审稿人论文组中出现的频数,确定论文选择审稿人的次序;然后,综合审稿人和论文间的亲和力得分以及审稿人对论文的研究方向覆盖得分,来计算审稿人对待审论文的综合审阅得分,并依据轮询调度得到论文预分配审稿小组;最后,对预分配审稿小组进行利益冲突检查与消解以实现最终的审稿小组推荐。实验结果表明,与基于松弛迭代的分配算法（FairIR）和同行评审公平分配算法（PR4A）等基于分配的审稿人推荐算法相比,所提算法在牺牲少量亲和力的情况下,将研究方向覆盖得分平均提高了38%,从而确保推荐结果更加准确合理。     

学术社交网络中的权威学者推荐模型

目前，学术社交网络平台存在的信息过载和信息不对称等问题导致学者特别是影响力低的学者很难找到自己感兴趣的内容，同时，学术社交网络中影响力大的学者对学术社区的形成具有一定的促进作用并且对影响力低的学者的科学研究具有一定的导向作用，因此提出一种融合学术社区检测的权威学者推荐模型（ISRMACD）来为学术社交网络中的低影响力学者提供推荐服务。首先，利用影响力大的学者圈作为社区的核心结构对学术社交网络中学者间的关系纽带——好友关系所产生的复杂网络拓扑关系进行学术社区检测；然后，对社区内的学者计算影响力，并实现社区内部的权威学者推荐服务。在学者网数据集上的实验结果表明，该推荐模型在不同的权威学者推荐数量下均取得了较高的推荐质量，并且每次推荐10名权威学者取得的推荐精度最高，达到70%及以上。     

学术社交网络中的权威学者推荐模型

目前,学术社交网络平台存在的信息过载和信息不对称等问题导致学者特别是影响力低的学者很难找到自己感兴趣的内容,同时,学术社交网络中影响力大的学者对学术社区的形成具有一定的促进作用并且对影响力低的学者的科学研究具有一定的导向作用,因此提出一种融合学术社区检测的权威学者推荐模型（ISRMACD）来为学术社交网络中的低影响力学者提供推荐服务。首先,利用影响力大的学者圈作为社区的核心结构对学术社交网络中学者间的关系纽带——好友关系所产生的复杂网络拓扑关系进行学术社区检测;然后,对社区内的学者计算影响力,并实现社区内部的权威学者推荐服务。在学者网数据集上的实验结果表明,该推荐模型在不同的权威学者推荐数量下均取得了较高的推荐质量,并且每次推荐10名权威学者取得的推荐精度最高,达到70%及以上。     

Music recommendation using text analysis on song requests to radio stations

Recommending appropriate music to users has always been a difficult task. In this paper, we propose a novel method in recommending music by analyzing the textual input of users. To this end, we mine a large corpus of documents from a Korean radio station’s online bulletin board. Each document, written by the listener, is composed of a song request associated with a brief, personal story. We assume that such stories are closely related with the background of the song requests and thus, our system performs text analysis to recommend songs that were requested from other similar stories. We evaluate our system using conventional metrics along with a user evaluation test. Results show that there is close correlation between document similarity and song similarity, indicating the potential of using text as a source to recommending music.     

基于协同过滤算法的自动化隐式评分音乐双重推荐系统

根据传统系统采用的显示评分机制受到外界干扰影响,使推荐结果精准度低的问题,提出了基于协同过滤算法的自动化隐式评分音乐双重推荐系统。在异构普适环境推荐框架下,对系统总体结构进行设计。其中硬件部分采用四元件组成方式,使用W900710型号芯片作为播放器核心板,并将隐式评分提取器与推荐引擎结合起来,可避免噪声干扰。而软件部分设计场景模拟衰减现象,采用协同过滤算法描述衰减过程,根据描述结果,设立双重推荐机制来实现抗人为影响的音乐双重推荐系统。由实验结果可知,采用协同过滤算法设计的系统最高推荐结果精准度可达到90%,对于大规模音乐数据推荐具有良好可扩展性。     

基于服务QoS执行信息的Web服务推荐研究

基于服务的非功能（QoS）特性进行服务发现是面向服务计算领域的一个研究热点。为了克服服务QoS难以获取且动态变化的特点,提出一种基于已往QoS执行信息的Web服务推荐方法。首先给出一种可以方便记录QoS执行信息的系统框架;然后描述了基于时间加权的服务推荐的匹配算法;实验结果表明该推荐方法是可行和有效的。     

基于SSD和时序模型的微博好友推荐算法

社交网络用户的指数型增长,导致用户在网络中难以找到适合自己的好友.提出一种基于多目标检测算法SSD和时序模型的微博好友推荐算法BSBT-FR,首先利用SSD对搜集到的用户图像进行信息提取,再利用时序模型在时间维度上对提取到的信息做进一步处理,然后利用JS散度公式计算用户间的相似度,最后与基于用户个人信息得出的相似度进行...     

基于不确定性知识发现的入侵报警关联方法

针对入侵检测系统报警信息量大、琐碎和分散的问题,提出了一种基于不确定性知识发现的入侵报警关联方法。该方法的知识发现部分采用提出的不确定性序列模式发现算法—CWINEPI对报警数据进行序列模式发现,并将经过筛选后获得的入侵报警序列模式转化成入侵报警精简规则;再对入侵报警序列模式进行关联以获取攻击模式,并转化为入侵场景重建规则。入侵报警关联部分利用获取的入侵报警精简规则和入侵场景重建规则,以模式匹配方法构造报警关联引擎,对多个入侵检测系统上报的入侵报警进行关联。美国国防部高级研究计划局2000年入侵评测数据（DARPA2000）的报警数据验证了知识发现部分的良好性能;测试环境中的入侵报警的关联结果表明了该方法是高效、可行的。     

A recommendation system for repairing violations detected by static architecture conformance checking

This paper describes a recommendation system that provides refactoring guidelines for maintainers when tackling architectural erosion. The paper formalizes 32 refactoring recommendations to repair violations raised by static architecture conformance checking approaches; it describes a tool—called ArchFix—that triggers the proposed recommendations; and it evaluates the application of this tool in two industrial‐strength systems. For the first system—a 21 KLOC open‐source strategic management system—our approach has indicated correct refactoring recommendations for 31 out of 41 violations detected as the result of an architecture conformance process. For the second system—a 728 KLOC customer care system used by a major telecommunication company—our approach has triggered correct recommendations for 624 out of 787 violations, as asserted by the system's architect. Moreover, the architects have scored 82% of these recommendations as having moderate or major complexity. Copyright © 2013 John Wiley & Sons, Ltd.     

A mood‐ and situation‐based model for developing intuitive Pop music recommendation systems

Traditionally, a per‐song‐purchased base recommendation system is used on most music websites, but this method produces unsatisfactory results under various situational practices. This study proposes a hybrid procedure that includes both an expert‐attributes selection capability and a mood/situation‐attributes categorization functionality. This procedure fosters the development of a so‐called MoMusic model as an unlimited online streaming service to replace current systems and artfully provide music to interested parties. This study employs a dataset consisting of 821 songs from the 2005–2010 annual music rankings as well as songs from the top artists from 2009 to 2010 from Taiwan's popular KKBOX music streaming service. The experimental dataset was assessed and coded by domain experts, and the expert‐attributes selections and mood/situation‐attributes categorizations were used to produce recommendation lists. These recommendation lists were then paired with questionnaire‐derived music preferences from experienced users. The experimental results conclusively show that the MoMusic model is approximately twice as accurate as the random selection‐based lists and the KKBOX‐like recommendation lists and performs better than the two listed recommendation systems. The MoMusic model scores 0.889 on the usefulness evaluation, whereas the system satisfaction is 0.96. The MoMusic model has the advantages of intuitive use and high performance.     

Controlled automated discovery of collections of business process models

Automated process discovery techniques aim at extracting process models from information system logs. Existing techniques in this space are effective when applied to relatively small or regular logs, but generate spaghetti-like and sometimes inaccurate models when confronted to logs with high variability. In previous work, trace clustering has been applied in an attempt to reduce the size and complexity of automatically discovered process models. The idea is to split the log into clusters and to discover one model per cluster. This leads to a collection of process models – each one representing a variant of the business process – as opposed to an all-encompassing model. Still, models produced in this way may exhibit unacceptably high complexity and low fitness. In this setting, this paper presents a two-way divide-and-conquer process discovery technique, wherein the discovered process models are split on the one hand by variants and on the other hand hierarchically using subprocess extraction. Splitting is performed in a controlled manner in order to achieve user-defined complexity or fitness thresholds. Experiments on real-life logs show that the technique produces collections of models substantially smaller than those extracted by applying existing trace clustering techniques, while allowing the user to control the fitness of the resulting models.