A web-based platform for biosignal visualization and annotation

With the advent of wearable sensing and mobile technologies, biosignals have seen an increasingly growing number of application areas, leading to the collection of large volumes of data. One of the difficulties in dealing with these data sets, and in the development of automated machine learning systems which use them as input, is the lack of reliable ground truth information. In this paper we present a new web-based platform for visualization, retrieval and annotation of biosignals by non-technical users, aimed at improving the process of ground truth collection for biomedical applications. Moreover, a novel extendable and scalable data representation model and persistency framework is presented. The results of the experimental evaluation with possible users has further confirmed the potential of the presented framework.     

GATE Teamware: a web-based, collaborative text annotation framework

This paper presents GATE Teamware—an open-source, web-based, collaborative text annotation framework. It enables users to carry out complex corpus annotation projects, involving distributed annotator teams. Different user roles are provided (annotator, manager, administrator) with customisable user interface functionalities, in order to support the complex workflows and user interactions that occur in corpus annotation projects. Documents may be pre-processed automatically, so that human annotators can begin with text that has already been pre-annotated and thus making them more efficient. The user interface is simple to learn, aimed at non-experts, and runs in an ordinary web browser, without need of additional software installation. GATE Teamware has been evaluated through the creation of several gold standard corpora and internal projects, as well as through external evaluation in commercial and EU text annotation projects. It is available as on-demand service on GateCloud.net, as well as open-source for self-installation.     

轻量化Transformer目标跟踪数据标注算法

目的 基于深度模型的跟踪算法往往需要大规模的高质量标注训练数据集,而人工逐帧标注视频数据会耗费大量的人力及时间成本。本文提出一个基于Transformer模型的轻量化视频标注算法（Transformer-based label network,TLNet）,实现对大规模稀疏标注视频数据集的高效逐帧标注。方法 该算法通过Transformer模型来处理时序的目标外观和运动信息,并融合前反向的跟踪结果。其中质量评估子网络用于筛选跟踪失败帧,进行人工标注;回归子网络则对剩余帧的初始标注进行优化,输出更精确的目标框标注。该算法具有强泛化性,能够与具体跟踪算法解耦,应用现有的任意轻量化跟踪算法,实现高效的视频自动标注。结果 在2个大规模跟踪数据集上生成标注。对于LaSOT （large-scale single object tracking）数据集,自动标注过程仅需约43 h,与真实标注的平均重叠率（mean intersection over union,mIoU）由0.824提升至0.871。对于TrackingNet数据集,本文使用自动标注重新训练3种跟踪算法,并在3个数据集上测试跟踪性能,使用本文标注训练的模型在跟踪性能上超过使用TrackingNet原始标注训练的模型。结论 本文算法TLNet能够挖掘时序的目标外观和运动信息,对前反向跟踪结果进行帧级的质量评估并进一步优化目标框。该方法与具体跟踪算法解耦,具有强泛化性,并能节省超过90%的人工标注成本,高效地生成高质量的视频标注。     

Enhancement of digital reading performance by using a novel web-based collaborative reading annotation system with two quality annotation filtering mechanisms

Collaboratively annotating digital texts allows learners to add valued information, share ideas, and create knowledge. However, excessive annotations and poor-quality annotations in a digital text may cause information overload and divert attention from the main content. The increased cognitive load ultimately reduces the effectiveness of collaborative annotations in promoting reading comprehension. Thus, this work develops a web-based collaborative reading annotation system (WCRAS-TQAFM) with two quality annotation filtering mechanisms—high-grade and master annotation filters—to promote the reading performance of learners. Ninety-seven students from three classes of a senior high school in Taiwan were invited to participate in an 80-min reading activity in which individual readers use WCRAS with or without annotation filters. Analytical results indicate that digital reading performance is significantly better in readers who use the high-grade annotation filter compared to those who read all annotations. Moreover, the high-grade annotation filter can enhance the reading comprehension of learners in all considered question types (i.e., recall, main idea, inference, and application). Also, the Cohen’s kappa statistics was used for assessing whether the annotation selected by the high-grade annotation filter is in agreement with the annotations selected by a domain expert. The statistic results indicate that the proposed high-grade annotation filter is valid to some degree. Finally, neither of the proposed quality annotation filtering approaches significantly reduces cognitive load.     

An event-based platform for collaborative threats detection and monitoring

Organizations must protect their information systems from a variety of threats. Usually they employ isolated defenses such as firewalls, intrusion detection and fraud monitoring systems, without cooperating with the external world. Organizations belonging to the same markets (e.g., financial organizations, telco providers) typically suffer from the same cyber crimes. Sharing and correlating information could help them in early detecting those crimes and mitigating the damages.The paper discusses the Semantic Room (SR) abstraction which enables the development of collaborative event-based platforms, on the top of Internet, where data from different information systems are shared, in a controlled manner, and correlated to detect and timely react to coordinated Internet-based security threats (e.g., port scans, botnets) and frauds. In order to show the flexibility of the abstraction, the paper proposes the design, implementation and validation of two SRs: an SR that detects inter-domain port scan attacks and an SR that enables an online fraud monitoring over the Italian territory. In both cases, the SRs use real data traces for demonstrating the effectiveness of the proposed approach. In the first SR, high detection accuracy and small detection delays are achieved whereas in the second, new fraud evidence and investigation instruments are provided to law enforcement agencies.     

A web-based platform for virtual screening

A fully integrated, web-based, virtual screening platform has been developed to allow rapid virtual screening of large numbers of compounds. ORACLE is used to store information at all stages of the process. The system includes a large database of historical compounds from high throughput screenings (HTS) chemical suppliers, ATLAS, containing over 3.1 million unique compounds with their associated physiochemical properties (ClogP, MW, etc.). The database can be screened using a web-based interface to produce compound subsets for virtual screening or virtual library (VL) enumeration. In order to carry out the latter task within ORACLE a reaction data cartridge has been developed. Virtual libraries can be enumerated rapidly using the web-based interface to the cartridge. The compound subsets can be seamlessly submitted for virtual screening experiments, and the results can be viewed via another web-based interface allowing ad hoc querying of the virtual screening data stored in ORACLE.     

A framework for automatic semantic video annotation

Hypermedia composite templates define generic structures of nodes and links to be added to a document composition, providing spatio-temporal synchronization semantics. This paper presents EDITEC, a graphical editor for hypermedia composite templates. EDITEC templates are based on the XTemplate 3.0 language. The editor was designed for offering a user-friendly visual approach. It presents a new method that provides several options for representing iteration structures graphically, in order to specify a certain behavior to be applied to a set of generic document components. The editor provides a multi-view environment, giving the user a complete control of the composite template during the authoring process. Composite templates can be used in NCL documents for embedding spatio-temporal semantics into NCL contexts. NCL is the standard declarative language used for the production of interactive applications in the Brazilian digital TV system and ITU H.761 IPTV services. Hypermedia composite templates could also be used in other hypermedia authoring languages offering new types of compositions with predefined semantics.     

Semi-supervised kernel density estimation for video annotation

Insufficiency of labeled training data is a major obstacle for automatic video annotation. Semi-supervised learning is an effective approach to this problem by leveraging a large amount of unlabeled data. However, existing semi-supervised learning algorithms have not demonstrated promising results in large-scale video annotation due to several difficulties, such as large variation of video content and intractable computational cost. In this paper, we propose a novel semi-supervised learning algorithm named semi-supervised kernel density estimation (SSKDE) which is developed based on kernel density estimation (KDE) approach. While only labeled data are utilized in classical KDE, in SSKDE both labeled and unlabeled data are leveraged to estimate class conditional probability densities based on an extended form of KDE. It is a non-parametric method, and it thus naturally avoids the model assumption problem that exists in many parametric semi-supervised methods. Meanwhile, it can be implemented with an efficient iterative solution process. So, this method is appropriate for video annotation. Furthermore, motivated by existing adaptive KDE approach, we propose an improved algorithm named semi-supervised adaptive kernel density estimation (SSAKDE). It employs local adaptive kernels rather than a fixed kernel, such that broader kernels can be applied in the regions with low density. In this way, more accurate density estimates can be obtained. Extensive experiments have demonstrated the effectiveness of the proposed methods.     

Hierarchical representation of video sequences for annotation

Video annotation is an important issue in video content management systems. Rapid growth of the digital video data has created a need for efficient and reasonable mechanisms that can ease the annotation process. In this paper, we propose a novel hierarchical clustering based system for video annotation. The proposed system generates a top-down hierarchy of the video streams using hierarchical k-means clustering. A tree-based structure is produced by dividing the video recursively into sub-groups, each of which consists of similar content. Based on the visual features, each node of the tree is partitioned into its children using k-means clustering. Each sub-group is then represented by its key frame, which is selected as the closest frame to the centroids of the corresponding cluster, and then can be displayed at the higher level of the hierarchy. The experiments show that very good hierarchical view of the video sequences can be created for annotation in terms of efficiency.     

基于日志的协同图像自动标注

反馈日志隐含的图像语义信息有助于图像自动标注,但日志数据中存在的噪声、片面性等问题制约了其作用,故提出基于日志的协同图像自动标注算法。根据日志获取的特点,采用增量关联规则挖掘处理日志信息去除其噪声,利用协同滤波思想扩展图像标注词数量,利用WordNet得到标注词间关系,并结合图像底层特征利用混合概率模型实现图像自动标注。在Corel5K和互联网数据集上的实验表明：该算法降低了日志噪声及片面性所带来的影响,提高了图像自动标注效率和质量。     

An expandable web-based platform for visually analyzing basin-scale hydro-climate time series data

Growing demand from the general public for centralized points of data access and analytics tools coincides with similar, well-documented needs of regional and international hydrology research and resource management communities. To address this need within the Laurentian Great Lakes region, we introduce the Great Lakes Dashboard (GLD), a dynamic web data visualization platform that brings multiple time series data sets together for visual analysis and download. The platform's adaptable, robust, and expandable Time Series Core Object Model (GLD-TSCOM) separates the growing complexity and size of Great Lakes data sets from the web application interface. Although the GLD-TSCOM is currently applied exclusively to Great Lakes data sets, the concepts and methods discussed here can be applied in other geographical and topical areas of interest.     

Towards effective video annotation: An approach to automatically link notes with video content

The characteristics of annotations, such as highlighting, context-based notes, and organization are difficult to translate from the traditional paper-based medium to the digital format. An added challenge is how to facilitate annotations on a digital video in a collaborative distance learning environment. To explore issues in video annotation, we developed a tool called Interactive Shared Education Environment (ISEE). ISEE automatically generates hyperlinked timestamps, which we called Smartlinks, to associate the notes with their video contents. A usability study with 59 participants, following up by a small-scale eye-tracking study, was conducted to explore users’ video note-taking behaviors and to examine the effect of the new Smartlink design. Our results showed that participants with Smartlink took fewer notes, focused less on video controls and more on video content than those without Smartlink. We believe the main benefit of Smartlink is that it may offload non-learning related cognitive loads and allow users to take better notes. Findings from this study on users’ video annotation behaviors shed light on the future design of video annotation systems in both individual and collaborative environments.     

A temporal context model for boosting video annotation

In this paper, we propose a new method to model the temporal context for boosting video annotation accuracy. The motivation of our idea mainly comes from the fact that temporally continuous shots in video are generally with relevant content, so that the performance of video annotation could be comparably boosted by mining the temporal dependency between shots in video. Based on this consideration, we propose a temporal context model to mine the redundant information between shots. By connecting our model with conditional random field and borrowing the learning and inference approaches from it, we could obtain the refined probability of a concept occurring in the shot, which is the leverage of temporal context information and initial output of video annotation. Comparing with existing methods for temporal context mining of video annotation, our model could capture different kinds of shot dependency more accurately to improve the video annotation performance. Furthermore, our model is relatively simple and efficient, which is important for the applications which have large scale data to process. Extensive experimental results on the widely used TRECVID datasets exhibit the effectiveness of our method for improving video annotation accuracy.     

Semantic context based refinement for news video annotation

Automatic video annotation is to bridge the semantic gap and facilitate concept based video retrieval by detecting high level concepts from video data. Recently, utilizing context information has emerged as an important direction in such domain. In this paper, we present a novel video annotation refinement approach by utilizing extrinsic semantic context extracted from video subtitles and intrinsic context among candidate annotation concepts. The extrinsic semantic context is formed by identifying a set of key terms from video subtitles. The semantic similarity between those key terms and the candidate annotation concepts is then exploited to refine initial annotation results, while most existing approaches utilize textual information heuristically. Similarity measurements including Google distance and WordNet distance have been investigated for such a refinement purpose, which is different with approaches deriving semantic relationship among concepts from given training datasets. Visualness is also utilized to discriminate individual terms for further refinement. In addition, Random Walk with Restarts (RWR) technique is employed to perform final refinement of the annotation results by exploring the inter-relationship among annotation concepts. Comprehensive experiments on TRECVID 2005 dataset have been conducted to demonstrate the effectiveness of the proposed annotation approach and to investigate the impact of various factors.     

Ontology-driven collaborative annotation in shared workspaces

In the last decade, collaboration and sharing on the Web have become mainstream. Digital, remote interaction happens on a daily basis, not only to share digital resources, but also to create, manage and discuss them, in every possible situation where collaboration is required: from work teams to groups of friends, from community committees to no-profit organizations. In this paper we address the task of collaborative management of digital resources within a team, with a special focus on the task of semantic annotation, where team members, possibly supported by automated reasoning, enrich resources with properties that help in organizing, retrieving and creating connections between contents of different types. We focus in particular on the problem of reaching an agreement on the annotation itself among the participants. The paper presents a qualitative user study aimed at observing users behavior when faced with this task. The results of the study are then analyzed in order to draw guidelines, which are then implemented in a tool for collaborative annotation. This study is carried out in the context of the Semantic Table Plus Plus (Sem T++) Project, a framework supporting collaboration over thematic workspaces, whose goal is to enhance cooperation through awareness, enhanced communication and easy sharing of digital content.     

Content annotation for the semantic web: an automatic web-based approach

Semantic Annotation is required to add machine-readable content to natural language text. A global initiative such as the Semantic Web directly depends on the annotation of massive amounts of textual Web resources. However, considering the amount of those resources, a manual semantic annotation of their contents is neither feasible nor scalable. In this paper we introduce a methodology to partially annotate textual content of Web resources in an automatic and unsupervised way. It uses several well-established learning techniques and heuristics to discover relevant entities in text and to associate them to classes of an input ontology by means of linguistic patterns. It also relies on the Web information distribution to assess the degree of semantic co-relation between entities and classes of the input domain ontology. Special efforts have been put in minimizing the amount of Web accesses required to evaluate entities in order to ensure the scalability of the approach. A manual evaluation has been carried out to test the methodology for several domains showing promising results.