Ontology-centered syndromic surveillance for bioterrorism

In recent years, public health surveillance has become a priority, driven by concerns of possible bioterrorist attacks and disease outbreaks. Authorities argue that syndromic surveillance, or the monitoring of prediagnostic health-related data for early detection of nascent outbreaks, is crucial to preventing massive illness and death. Syndromic surveillance could prevent widespread illness and death, but public-health analysts face many technical barriers. To meet syndromic surveillance's complex operational and research needs, and as part of DARPA'S national biosurveillance technology program, we've developed BioSTORM (the biological spatio-temporal outbreak reasoning module). BioSTORM is an experimental end-to-end computational framework that integrates disparate data sources and deploys various analytic problem solvers to support public health analysts in interpreting surveillance data and identifying disease outbreaks. BioSTORM can help them by supporting ontology-based data integration and problem-solver deployment.     

基于改进局部线性特征编码方法的图像分类

特征编码是利用广义视觉词袋模型获得图像稀疏表示的关键步骤。本文研究了两种常用的局部线性特征编码方法即LLC及NSLLC编码方法,并针对其存在的问题,提出了一种利用编码系数非负性约束对其进行改进的方法——NNLLC,并将其应用于图像分类任务中。实验结果表明,该方法能有效改进局部线性特征编码性能,提高图像特征的可区分性,相比于LLC及NSLLC特征编码方法,在图像分类任务中取得了更高的平均分类准确率。     

基于局部对称性的特征点加工策略及应用

在基于词袋模型的图像检索框架中,图像包含的SIFT特征点往往数量比较大,特征不够强。因此图像检索系统的效率和性能往往受影响。基于SIFT特征点的性质和视觉显著性原理,提出了SIFT特征点的局部对称性度量方法,并且在图像检索框架中嵌入了基于对称性的SIFT特征点过滤方法和加权策略,以提升SIFT特征点的利用效率。在牛津大学建筑物图像集上的实验结果表明,提出的基于对称性的SIFT特征点选择策略能有效地提高图像检索的性能。     

A multilingual ontology for infectious disease surveillance: rationale, design and challenges

A lack of surveillance system infrastructure in the Asia-Pacific region is seen as hindering the global control of rapidly spreading infectious diseases such as the recent avian H5N1 epidemic. As part of improving surveillance in the region, the BioCaster project aims to develop a system based on text mining for automatically monitoring Internet news and other online sources in several regional languages. At the heart of the system is an application ontology which serves the dual purpose of enabling advanced searches on the mined facts and of allowing the system to make intelligent inferences for assessing the priority of events. However, it became clear early on in the project that existing classification schemes did not have the necessary language coverage or semantic specificity for our needs. In this article we present an overview of our needs and explore in detail the rationale and methods for developing a new conceptual structure and multilingual terminological resource that focusses on priority pathogens and the diseases they cause. The ontology is made freely available as an online database and downloadable OWL file.     

Exploring the Trade-off Between Accuracy and Observational Latency in Action Recognition

An important aspect in designing interactive, action-based interfaces is reliably recognizing actions with minimal latency. High latency causes the system’s feedback to lag behind user actions and thus significantly degrades the interactivity of the user experience. This paper presents algorithms for reducing latency when recognizing actions. We use a latency-aware learning formulation to train a logistic regression-based classifier that automatically determines distinctive canonical poses from data and uses these to robustly recognize actions in the presence of ambiguous poses. We introduce a novel (publicly released) dataset for the purpose of our experiments. Comparisons of our method against both a Bag of Words and a Conditional Random Field (CRF) classifier show improved recognition performance for both pre-segmented and online classification tasks. Additionally, we employ GentleBoost to reduce our feature set and further improve our results. We then present experiments that explore the accuracy/latency trade-off over a varying number of actions. Finally, we evaluate our algorithm on two existing datasets.     

一种基于语义标注特征的金融文本分类方法

针对基于词袋的机器学习文本分类方法所存在的：高维度、高稀疏性、不能识别同义词、语义信息缺失等问题,和基于规则模式的文本分类所存在的虽然准确率较高但鲁棒性较差的问题,本文提出了一种采用词汇-语义规则模式从金融新闻文本中提取事件语义标注信息,并将其作为分类特征用于机器学习文本分类中的新方法。实验证明采用该方法相比基于词袋的文本分类方法在采用相同的特征选择算法和分类算法的基础上,F1值提高8.6 %,查准率提高7.7% ,查全率提高8.8%。本文方法融合了知识驱动和数据驱动在文本分类中的优点,同时避免了它们所存在的主要缺点,具有显著的实用性和研究参考价值。     

Knowle: A semantic link network based system for organizing large scale online news events

An explosive growth in the volume, velocity, and variety of the data available on the Internet has been witnessed recently. The data originated from multiple types of sources including mobile devices, sensors, individual archives, social networks, Internet of Things, enterprises, cameras, software logs, health data has led to one of the most challenging research issues of the big data era. In this paper, Knowle—an online news management system upon semantic link network model is introduced. Knowle is a news event centrality data management system. The core elements of Knowle are news events on the Web, which are linked by their semantic relations. Knowle is a hierarchical data system, which has three different layers including the bottom layer (concepts), the middle layer (resources), and the top layer (events). The basic blocks of the Knowle system—news collection, resources representation, semantic relations mining, semantic linking news events are given. Knowle does not require data providers to follow semantic standards such as RDF or OWL, which is a semantics-rich self-organized network. It reflects various semantic relations of concepts, news, and events. Moreover, in the case study, Knowle is used for organizing and mining health news, which shows the potential on forming the basis of designing and developing big data analytics based innovation framework in the health domain.     

BioWar: scalable agent-based model of bioattacks

While structured by social and institutional networks, disease outbreaks are modulated by physical, economical, technological, communication, health, and governmental infrastructures. To systematically reason about the nature of outbreaks, the potential outcomes of media, prophylaxis, and vaccination campaigns, and the relative value of various early warning devices, social context, and infrastructure, must be considered. Numerical models provide a cost-effective ethical system for reasoning about such events. BioWar, a scalable citywide multiagent network numerical model, is described in this paper. BioWar simulates individuals as agents who are embedded in social, health, and professional networks and tracks the incidence of background and maliciously introduced diseases. In addition to epidemiology, BioWar simulates health-care-seeking behaviors, absenteeism patterns, and pharmaceutical purchases, information useful for syndromic and behavioral surveillance algorithms.     

多特征融合的场景图像分类算法

提出了一种Gabor-LBP频域纹理特征与词包模型语义特征相结合的场景图像分类算法.利用Gabor变换得到的频域信息,及对应的LBP特征,与视觉词包模型（BOW）提取的语义特征自适应相融合,实现分类.为了验证本文算法,利用两个标准图像测试库进行比较测试,实验结果表明,本文算法在改善图像纹理表达上具有明显优势,特别是对于图像的光照、旋转、尺度都具有很好的鲁棒性.     

Combining experts for anchorperson shot detection in news videos

Automatic classification of shots extracted by news videos plays an important role in the context of news video segmentation, which is an essential step towards effective indexing of broadcasters digital databases. In spite of the efforts reported by the researchers involved in this field, no techniques providing fully satisfactory performance have been presented until now. In this paper, we propose a multi-expert approach for unsupervised shot classification. The proposed multi-expert system (MES) combines three algorithms that are model-free and do not require a specific training phase. In order to assess the performance of the MES, we built up a database significantly wider than those typically used in the field. Experimental results demonstrate the effectiveness of the proposed approach both in terms of shot classification and of news story detection capability.     

新闻视频中基于主持人识别的新闻故事探测

新闻视频由一个个内容相互独立的新闻故事组成。新闻故事探测是新闻视频浏览、基于内容检索等操作的前提。该文根据新闻视频的特殊结构和新闻节目主持人固定的特征，采用基于人脸检测的主持人镜头识别和基于语音的主持人识别来分割新闻视频中的新闻故事。实验表明，该方法能准确地探测出新闻视频中的新闻故事。     

Automatic detection of potential mosquito breeding sites from aerial images acquired by unmanned aerial vehicles

The World Health Organization (WHO) has stated that effective vector control measures are critical to achieving and sustaining reduction of vector-borne infectious disease incidence. Unmanned aerial vehicles (UAVs), popularly known as drones, can be an important technological tool for health surveillance teams to locate and eliminate mosquito breeding sites in areas where vector-borne diseases such as dengue, zika, chikungunya or malaria are endemic, since they allow the acquisition of aerial images with high spatial and temporal resolution. Currently, though, such images are often analyzed through manual processes that are excessively time-consuming when implementing vector control interventions. In this work we propose computational approaches for the automatic identification of objects and scenarios suspected of being potential mosquito breeding sites from aerial images acquired by drones. These approaches were developed using convolutional neural networks (CNN) and Bag of Visual Words combined with the Support Vector Machine classifier (BoVW + SVM), and their performances were evaluated in terms of mean Average Precision - mAP-50. In the detection of objects using a CNN YOLOv3 model the rate of 0.9651 was obtained for the mAP-50. In the detection of scenarios, in which the performances of BoVW+SVM and a CNN YOLOv3 were compared, the respective rates of 0.6453 and 0.9028 were obtained. These findings indicate that the proposed CNN-based approaches can be used to identify potential mosquito breeding sites from images acquired by UAVs, providing substantial improvements in vector control programs aiming the reduction of mosquito-breeding sources in the environment.     

Towards heart sound classification without segmentation via autocorrelation feature and diffusion maps

Heart sound classification, used for the automatic heart sound auscultation and cardiac monitoring, plays an important role in primary health center and home care. However, one of the most difficult problems for the task of heart sound classification is the heart sound segmentation, especially for classifying a wide range of heart sounds accompanied with murmurs and other artificial noise in the real world. In this study, we present a novel framework for heart sound classification without segmentation based on the autocorrelation feature and diffusion maps, which can provide a primary diagnosis in the primary health center and home care. In the proposed framework, the autocorrelation features are first extracted from the sub-band envelopes calculated from the sub-band coefficients of the heart signal with the discrete wavelet decomposition (DWT). Then, the autocorrelation features are fused to obtain the unified feature representation with diffusion maps. Finally, the unified feature is input into the Support Vector Machines (SVM) classifier to perform the task of heart sound classification. Moreover, the proposed framework is evaluated on two public datasets published in the PASCAL Classifying Heart Sounds Challenge. The experimental results show outstanding performance of the proposed method, compared with the baselines.     

Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton

Feature selection plays an important role in the machine-vision-based online detection of foreign fibers in cotton because of improvement detection accuracy and speed. Feature sets of foreign fibers in cotton belong to multi-character feature sets. That means the high-quality feature sets of foreign fibers in cotton consist of three classes of features which are respectively the color, texture and shape features. The multi-character feature sets naturally contain a space constraint which lead to the smaller feature space than the general feature set with the same number of features, however the existing algorithms do not consider the space characteristic of multi-character feature sets and treat the multi-character feature sets as the general feature sets. This paper proposed an improved ant colony optimization for feature selection, whose objective is to find the (near) optimal subsets in multi-character feature sets. In the proposed algorithm, group constraint is adopted to limit subset constructing process and probability transition for reducing the effect of invalid subsets and improve the convergence efficiency. As a result, the algorithm can effectively find the high-quality subsets in the feature space of multi-character feature sets. The proposed algorithm is tested in the datasets of foreign fibers in cotton and comparisons with other methods are also made. The experimental results show that the proposed algorithm can find the high-quality subsets with smaller size and high classification accuracy. This is very important to improve performance of online detection systems of foreign fibers in cotton.     

公共卫生地理信息的应用与展望

传染病防治已不再是单一国家的问题,全球任何一地的疫情也随时可能在下一刻影响到自己国家,因此完善的疾病监测体系成为最重要的防疫武器,而地理信息系统在其中扮演了关键的角色。经由疾病病例的时空分布可视化,即能快速辅助防疫策略的规划、施行与评估,达成决策支持的目标,另外整合空间统计方法的运用,可系统化与科学化地从大量历史资料中侦测出异常事件,以便公共卫生人员进行研判与调查。由于互联网、移动设备、社交媒体的大量普及使用而建立起了新兴社交网络,开启了由下而上的民众监测体系,跳脱了传统式官方自上而下的权威式公共卫生治理,不仅强化民众参与,更能从社区角度提早至病患就医前就能掌握疫情走向,提升社区侦测效果,再辅以空间资讯对风险区提早做适当的介入。因此地理信息系统除了传统回溯性的疾病聚集研究外,也能有前瞻性防杜下一波流行的功效。     

Scalable news recommendation using multi-dimensional similarity and Jaccard–Kmeans clustering

In order to solve the scalability problem in news recommendation, a scalable news recommendation method is proposed. The method includes the multi-dimensional similarity calculation, the Jaccard–Kmeans fast clustering and the Top-N recommendation. The multi-dimensional similarity calculation method is used to compute the integrated similarity between users, which considers abundant content feature of news, behaviors of users, and the time of these behaviors occurring. Based on traditional K-means algorithm, the Jaccard–Kmeans fast clustering method is proposed. This clustering method first computes the above multi-dimensional similarity, then generates multiple cluster centers with user behavior feature and news content feature, and evaluates the clustering results according to cohesiveness. The Top-N recommendation method integrates a time factor into the final recommendation. Experiment results prove that the proposed method can enhance the scalability of news recommendation, significantly improve the recommendation accuracy in condition of data sparsity, and improve the timeliness of news recommendation.     

Farsi/Arabic handwritten digit recognition using quantum neural networks and bag of visual words method

Handwritten digit recognition has long been a challenging problem in the field of optical character recognition and of great importance in industry. This paper develops a new approach for handwritten digit recognition that uses a small number of patterns for training phase. To improve performance of isolated Farsi/Arabic handwritten digit recognition, we use Bag of Visual Words (BoVW) technique to construct images feature vectors. Each visual word is described by Scale Invariant Feature Transform (SIFT) method. For learning feature vectors, Quantum Neural Networks (QNN) classifier is used. Experimental results on a very popular Farsi/Arabic handwritten digit dataset (HODA dataset) show that proposed method can achieve the highest recognition rate compared to other state of the arts methods.     

基于J2EE的新闻发布系统的分析与设计

本文针对J2EE技术进行深入研究,对Struts框架进行深入剖析,研究Struts框架的内部结构、运作流程,以及对MVC设计模式的分析研究和对MYSQL数据库进行深入了解,并提出了一个以J2EE为平台,使用Struts框架,引入MVC设计模式的基于J2EE的新闻发布系统。本系统提高了用户获取新闻信息的及时性,使用户能更...