Multivalued contexts associated with criteria

We are interested in the study of L-fuzzy contexts taking into account different criteria. These contexts arise when we want to analyze the relationship between objects and attributes from different points of view. Furthermore, in some occasions, these L-fuzzy contexts have several values for every pair object attribute. We will see how both the WOWA operators and the Choquet integrals will be interesting tools for the aggregation processes that we are going to carry out.     

Using Personal Traces in Context Space: Towards Context Trace Technology

Wearables are often described with a focus on providing the user with wearable information access and communication means. The contextual information retrieval aspect is, however, an essential feature of such systems, as in, for example, the Remembrance Agent [1] where manually entered search-terms are used for presenting relevant situational information, or as in different location-based systems [2]. In this position paper we outline a general framework of contextually aware wearable systems, and suggest how such mechanisms, collecting massive traces of the user context, may lead to several other interesting uses in what we will call context trace technology.     

The ontology of dimensionality for anthropological database modeling

The properties of complexly structured application domains on anthropology have been considered, with the relevant limit conditions being specified. It has been shown that if these conditions are taken into consideration, the centuries-old reproducibility of anthropological databases by color canons, idioms and/or markers serves as a reliable foundation for building specialized and/or general ontologies with attributes as color propositions and concepts differentiated according to systems’ chromatic planes. To combine specialized ontologies with diverse objects and/or their attributes, a dimensionality criterion was introduced for the adequateness of relationships, which included substantial categories of space [L], information [I], and time [T]. The use of color concepts, planes and [LIT] of the system of dimensionalities has allowed us to describe information models of anthropological databases with diverse attributes.     

A framework for incremental construction of real global smart space applications

This article describes a standardised way to build context-aware global smart space applications using information that is distributed across independent (legacy, sensor-enabled, and embedded) systems by exploiting the overlapping spatial and temporal attributes of the information maintained by these systems. The framework supports a spatial programming model based on a topographical approach to modelling space that enables systems to independently define and use potentially overlapping spatial context in a consistent manner and in contrast to topological approaches, in which geographical relationships between objects are described explicitly. This approach is supported by an extensible data model that implicitly captures the relationships between information provided by separate underlying systems and facilitates the incremental construction of global smart spaces since the underlying systems to be incorporated are largely decoupled. The framework has been evaluated using a prototype that integrates legacy systems and context-aware services for multi-modal urban journey planning and for visualising traffic congestion.     

实时视觉目标跟踪与视频对象分割多任务框架

目的 针对视觉目标跟踪（video object tracking,VOT）和视频对象分割（video object segmentation,VOS）问题,研究人员提出了多个多任务处理框架,但是该类框架的精确度和鲁棒性较差。针对此问题,本文提出一个融合多尺度上下文信息和视频帧间信息的实时视觉目标跟踪与视频对象分割多任务的端到端框架。方法 文中提出的架构使用了由空洞深度可分离卷积组成的更加多尺度的空洞空间金字塔池化模块,以及具备帧间信息的帧间掩模传播模块,使得网络对多尺度目标对象分割能力更强,同时具备更好的鲁棒性。结果 本文方法在视觉目标跟踪VOT-2016和VOT-2018数据集上的期望平均重叠率（expected average overlap,EAO）分别达到了0.462和0.408,分别比SiamMask高了0.029和0.028,达到了最先进的结果,并且表现出更好的鲁棒性。在视频对象分割DAVIS（densely annotated video segmentation）-2016和DAVIS-2017数据集上也取得了有竞争力的结果。其中,在多目标对象分割DAVIS-2017数据集上,本文方法比SiamMask有更好的性能表现,区域相似度的杰卡德系数的平均值J_M和轮廓精确度的F度量的平均值F_M分别达到了56.0和59.0,并且区域和轮廓的衰变值J_D和F_D都比SiamMask中的低,分别为17.9和19.8。同时运行速度为45帧/s,达到了实时的运行速度。结论 文中提出的融合多尺度上下文信息和视频帧间信息的实时视觉目标跟踪与视频对象分割多任务的端到端框架,充分捕捉了多尺度上下文信息并且利用了视频帧间的信息,使得网络对多尺度目标对象分割能力更强的同时具备更好的鲁棒性。     

Fuzzy Conditional Probability Relations and their Applications in Fuzzy Information Systems

In our real-world applications, data may be imprecise in which levels or degrees of preciseness of data are intuitively different. In this case, fuzzy set expressions are considered as an alternative to represent the imprecise data. In general, the degree of similarity relationship between two fuzzy (imprecise) data in real-world applications may not necessarily be symmetric or transitive. In order to provide such a degree of similarity between two fuzzy data, we introduced the fuzzy conditional probability relation. The concept of a fuzzy conditional probability relation may be considered as a concrete example of weak similarity relation which in turn is a special type of fuzzy binary relation generalizing similarity relation. Two important applications concerning the application of Knowledge Discovery and Data Mining (KDD) in the presence of a fuzzy data table (usually called fuzzy information system), namely removing redundant objects and recognizing partial or total dependency of (domain) attributes, are considered induced by the fuzzy conditional probability relation. Here, the fuzzy information system contains precise as well as imprecise data (fuzzy values) about objects of interest characterized by some attributes. Related to the dependency of attributes, we introduce the fuzzy functional dependency that satisfies Armstrongs Axioms. In addition, we also discuss some interesting applications such as approximate data reduction and projection, approximate data querying and approximate joining in order to extend the query system.     

Cognitive ergonomics for data analysis. Experimental study of cognitive limitations in a data-based judgement task

ABSTRACT

Today’s ever-increasing amount of data places new demands on cognitive ergonomics and requires new design ideas to ensure successful human–data interaction. Our aim was to identify the cognitive factors that must be considered when designing systems to improve decision-making based on large amounts of data. We constructed a task that simulates the typical cognitive demands people encounter in data analysis situations. We demonstrate some essential cognitive limitations using a behavioural experiment with 20 participants. The studied task presented the participants with critical and noncritical attributes that contained information on two groups of people. They had to select the response option (group) with the higher level of critical attributes. The results showed that accuracy of judgement decreased as the amount of information increased, and that judgement was affected by irrelevant information. Our results thus demonstrate critical cognitive limitations when people utilise data and suggest a cognitive bias in data-based decision-making. Therefore, when designing for cognition, we should consider the human cognitive limitations that are manifested in a data analysis context. Furthermore, we need general cognitive ergonomic guidelines for design that support the utilisation of data and improve data-based decision-making.     

Augmenting Node-Link Diagrams with Topographic Attribute Maps

We propose a novel visualization technique for graphs that are attributed with scalar data. In many scenarios, these attributes (e.g., birth date in a family network) provide ambient context information for the graph structure, whose consideration is important for different visual graph analysis tasks. Graph attributes are usually conveyed using different visual representations (e.g., color, size, shape) or by reordering the graph structure according to the attribute domain (e.g., timelines). While visual encodings allow graphs to be arranged in a readable layout, assessing contextual information such as the relative similarities of attributes across the graph is often cumbersome. In contrast, attribute-based graph reordering serves the comparison task of attributes, but typically strongly impairs the readability of the structural information given by the graph's topology. In this work, we augment force-directed node-link diagrams with a continuous ambient representation of the attribute context. This way, we provide a consistent overview of the graph's topological structure as well as its attributes, supporting a wide range of graph-related analysis tasks. We resort to an intuitive height field metaphor, illustrated by a topographic map rendering using contour lines and suitable color maps. Contour lines visually connect nodes of similar attribute values, and depict their relative arrangement within the global context. Moreover, our contextual representation supports visualizing attribute value ranges associated with graph nodes (e.g., lifespans in a family network) as trajectories routed through this height field. We discuss how user interaction with both the structural and the contextual information fosters exploratory graph analysis tasks. The effectiveness and versatility of our technique is confirmed in a user study and case studies from various application domains.     

WYTIWYR: A User Intent-Aware Framework with Multi-modal Inputs for Visualization Retrieval

Retrieving charts from a large corpus is a fundamental task that can benefit numerous applications such as visualization recommendations. The retrieved results are expected to conform to both explicit visual attributes (e.g., chart type, colormap) and implicit user intents (e.g., design style, context information) that vary upon application scenarios. However, existing example-based chart retrieval methods are built upon non-decoupled and low-level visual features that are hard to interpret, while definition-based ones are constrained to pre-defined attributes that are hard to extend. In this work, we propose a new framework, namely WYTIWYR (What-You-Think-Is-What-You-Retrieve), that integrates user intents into the chart retrieval process. The framework consists of two stages: first, the Annotation stage disentangles the visual attributes within the query chart; and second, the Retrieval stage embeds the user's intent with customized text prompt as well as bitmap query chart, to recall targeted retrieval result. We develop aprototype WYTIWYR system leveraging a contrastive language-image pre-training (CLIP) model to achieve zero-shot classification as well as multi-modal input encoding, and test the prototype on a large corpus with charts crawled from the Internet. Quantitative experiments, case studies, and qualitative interviews are conducted. The results demonstrate the usability and effectiveness of our proposed framework.     

特征注意金字塔调制网络的视频目标分割

目的 视频目标分割是在给定第1帧标注对象掩模条件下,实现对整个视频序列中感兴趣目标的分割。但是由于分割对象尺度的多样性,现有的视频目标分割算法缺乏有效的策略来融合不同尺度的特征信息。因此,本文提出一种特征注意金字塔调制网络模块用于视频目标分割。方法 首先利用视觉调制器网络和空间调制器网络学习分割对象的视觉和空间信息,并以此为先验引导分割模型适应特定对象的外观。然后通过特征注意金字塔模块挖掘全局上下文信息,解决分割对象多尺度的问题。结果 实验表明,在DAVIS 2016数据集上,本文方法在不使用在线微调的情况下,与使用在线微调的最先进方法相比,表现出更具竞争力的结果,J-mean指标达到了78.7%。在使用在线微调后,本文方法的性能在DAVIS 2017数据集上实现了最好的结果,J-mean指标达到了68.8%。结论 特征注意金字塔调制网络的视频目标分割算法在对感兴趣对象分割的同时,针对不同尺度的对象掩模能有效结合上下文信息,减少细节信息的丢失,实现高质量视频对象分割。     

The axiomatic characterizations on L-fuzzy covering-based approximation operators

Axiomatic characterization is the foundation of L-fuzzy rough set theory: the axiom sets of approximation operators guarantee the existence of L-fuzzy relations or L-fuzzy coverings that reproduce the approximation operators. Axiomatic characterizations of approximation operators based on L-fuzzy coverings have not been fully explored, although those based on L-fuzzy relations have been studied thoroughly. Focusing on three pairs of widely used L-fuzzy covering-based approximation operators, we establish an axiom set for each of them, and their independence is examined. It should be noted that the axiom set of each L-fuzzy covering-based approximation operator is different from its crisp counterpart, with an either new or stronger axiom included in the L-fuzzy version.     

Logical foundations for representing object-oriented systems

Abstract

Object-oriented programming languages are designed for computing or simulating the behaviour of interacting objects, but their encapsulated contexts and procedural methods are not well suited to non-procedural techniques in theorem provers, optimizers, and automated design and analysis tools. Logic is the non-procedural system par excellence, but the predicate calculus notation for logic is awkward for representing and reasoning about encapsulated contexts. Conceptual graphs are a graphic system of logic that is better suited to O-O systems. First, they explicitly represent the contexts that are ignored or obscured in predicate calculus. Second, Peirce's rules of inference for reasoning with graphs are explicitly formulated in terms of contexts and the conditions for importing and exporting information from contexts. This article describes the context mechanisms of conceptual graphs, the rules of inference for reasoning with the graphs, and their use as a design language for object-oriented systems.     

Top-k dominating queries on incomplete large dataset

Top-k dominating (TKD) query is one of the methods to find the interesting objects by returning the k objects that dominate other objects in a given dataset. Incomplete datasets have missing values in uncertain dimensions, so it is difficult to obtain useful information with traditional data mining methods on complete data. BitMap Index Guided Algorithm (BIG) is a good choice for solving this problem. However, it is even harder to find top-k dominance objects on incomplete big data. When the dataset is too large, the requirements for the feasibility and performance of the algorithm will become very high. In this paper, we proposed an algorithm to apply MapReduce on the whole process with a pruning strategy, called Efficient Hadoop BitMap Index Guided Algorithm (EHBIG). This algorithm can realize TKD query on incomplete datasets through BitMap Index and use MapReduce architecture to make TKD query possible on large datasets. By using the pruning strategy, the runtime and memory usage are greatly reduced. What’s more, we also proposed an improved version of EHBIG (denoted as IEHBIG) which optimizes the whole algorithm flow. Our in-depth work in this article culminates with some experimental results that clearly show that our proposed algorithm can perform well on TKD query in an incomplete large dataset and shows great performance in a Hadoop computing cluster.

     

基于概念格因子分解的零件三维CAD模型检索

针对影响概念格应用的重要问题—即使是一个小规模数据集也会产生大量的形式概念,文中提出了可以满足关系覆盖的用对象（属性）概念分解形式背景对应的布尔矩阵的新方法.用这种方法原对象属性间的二元关系可以用数量在对象（属性）概念个数以内的概念表达出来,成为概念格因子.文中给出了概念格因子生成的基本原理及其算法.通过分析三维CAD零件模型功能表面间的关系构建零件工程图结构模型,并将其映射为形式背景,从而完成概念格因子到零件关键结构的应用.最后,实例演示了概念格因子在基于零件工程图结构模型的零件CAD模型检索中的运用.     

Distribution-based aggregation for relational learning with identifier attributes

Identifier attributes—very high-dimensional categorical attributes such as particular product ids or people's names—rarely are incorporated in statistical modeling. However, they can play an important role in relational modeling: it may be informative to have communicated with a particular set of people or to have purchased a particular set of products. A key limitation of existing relational modeling techniques is how they aggregate bags (multisets) of values from related entities. The aggregations used by existing methods are simple summaries of the distributions of features of related entities: e.g., MEAN, MODE, SUM, or COUNT. This paper's main contribution is the introduction of aggregation operators that capture more information about the value distributions, by storing meta-data about value distributions and referencing this meta-data when aggregating—for example by computing class-conditional distributional distances. Such aggregations are particularly important for aggregating values from high-dimensional categorical attributes, for which the simple aggregates provide little information. In the first half of the paper we provide general guidelines for designing aggregation operators, introduce the new aggregators in the context of the relational learning system ACORA (Automated Construction of Relational Attributes), and provide theoretical justification. We also conjecture special properties of identifier attributes, e.g., they proxy for unobserved attributes and for information deeper in the relationship network. In the second half of the paper we provide extensive empirical evidence that the distribution-based aggregators indeed do facilitate modeling with high-dimensional categorical attributes, and in support of the aforementioned conjectures. Editors: Hendrik Blockeel, David Jensen and Stefan Kramer An erratum to this article is available at .     

Integrating multiple information representations in a single CAD/CAM/CAE environment

The number of Computer-Aided Design, Computer-Aided Manufacturing, and Computer-Aided Engineering (CAD/CAM/CAE) programs and their capabilities has risen greatly in recent times. The programming efforts continue to be significant as each application developer strives to integrate more functionality. This paper describes a study on module integration in CAD/CAM/CAE software that considers how software can be structured to enable and ease the integration. This paper deals specifically with the issue of which object-oriented data structure is best for integration to minimize code execution times, memory usage, and programming effort. That is, we are interested in identifying the ideal base class for information objects. The study compares scene-graph and Information Model representations, compares two object referencing approaches (by path and direct object references), four modes of information interpretation, and three integration schemes. The study concludes that the ideal system combines scene-graph and Information Model approaches into one new flexible approach in which the information object base class contains a generically traversable array holding schema and user attributes. In general, objects should be referenced by a path from the hierarchy root object. Object information should be interpreted into an application context by a third object or interface. New information objects from a secondary representation should be embedded into the information hierarchy of the primary representation when they do not need to reference other objects, otherwise multiple inheritance or Component Object Modeling should be used to combine the corresponding objects from multiple representations into a single new object.

Using B<Superscript>+</Superscript>-trees for processing of line segments in large spatial databases

Points, lines, and regions are the three basic entities for constituting vector-based objects in spatial databases. Many indexing methods (G-tree, K-D-B tree, Quad-tree, PMR-tree, Grid-file, R-tree, and so on) have been widely discussed for handling point or region data. These traditional methods can efficiently organize point or region objects in a space into a hashing or hierarchical directory. They provide efficient access methods to meet the requirement of accurate retrievals. However, two problems are encountered when their techniques are applied to deal with line segments. The first is that representing line segments by means of point or region objects cannot exactly and properly preserve the spatial information about the proximities of line segments. The second problem is derived from the large dead space and overlapping areas in external and internal nodes of the hierarchical directory caused by the use of rectangles to enclose line objects. In this paper, we propose an indexing structure for line segments based on B ⁺ -tree to remedy these two problems. Through the experimental results, we demonstrate that our approach has significant improvement over the storage efficiency. In addition, the retrieval efficiency has also been significantly prompted as compared to the method using R-tree index scheme. These improvements derive mainly from the proposed data processing techniques and the new indexing method.     

Solving Failing Queries through Cooperation and Collaboration

Sometime Query Answering Systems (QAS) for a Distributed Autonomous Information System (DAIS) may fail by returning the empty set of objects as an answer for a query q. Systems in DAIS can be incomplete, have hierarchical attributes, and the semantics of attributes and their values may differ between sites. Also, if there are no objects in S matching q, the query may fail when submitted to S. Alternatively, QAS for S may try to relax the query q as it was proposed in T. Gaasterland (IEEE Expert, 12(5), 1997, 48–59), P. Godfrey (International Journal of Cooperative Information Systems, 6(2), 1997, 95–149) and W. Chu et al. (Journal of Intelligent Information Systems, 6(2/3), 1996, 223–259). It means that q can be replaced by a new more general query. Clearly, the goal is to find possibly the smallest generalization of q which will not fail in S. Smaller generalizations guarantee higher confidence in objects returned by QAS. Such QAS is called cooperative (only one site is involved). Queries may also fail in S when some of the attributes listed in q are outside the domain of S. To resolve this type of queries, assuming that S is a part of DAIS, we may extract definitions of such attributes from information systems residing at some of the remote sites for S and next use them to approximate q in S. In order to do that successfully, we assume that all involved systems have to agree on the ontology of some of their common attributes Z.W. Raś and A. Dardzińska (Information Systems International Journal, 29(1), 2004, 47–58; Proceedings of FQAS 2004 Conference, LNCS/LNAI No. 3055, 2004, pp. 125–136); Z.W. Raś and S. Joshi, Fundamenta Informaticae Journal, 30(3/4), 1997, 313–324. QAS based on the above strategy is called collaborative (minimum two sites are involved). Similarly, a query may fail in S when the granularity of an attribute used in q is finer than the granularity of the same attribute in S. This paper shows how to use collaboration and cooperation approach to solve failing queries in DAIS assuming that attributes are hierarchical. Some aspects of a collaboration strategy dealing with failing query problem for non-hierarchical attributes have been presented in Z.W. Raś and A. Dardzińska (Information Systems International Journal, 29(1), 2004, 47–58; Proceedings of FQAS 2004 Conference, LNCS/LNAI No. 3055, 2004, pp. 125–136).     

Tracing Evolving Subspace Clusters in Temporal Climate Data

Analysis of temporal climate data is an active research area. Advanced data mining methods designed especially for these temporal data support the domain expert’s pursuit to understand phenomena as the climate change, which is crucial for a sustainable world. Important solutions for mining temporal data are cluster tracing approaches, which are used to mine temporal evolutions of clusters. Generally, clusters represent groups of objects with similar values. In a temporal context like tracing, similar values correspond to similar behavior in one snapshot in time. Each cluster can be interpreted as a behavior type and cluster tracing corresponds to tracking similar behaviors over time. Existing tracing approaches are for datasets satisfying two specific conditions: The clusters appear in all attributes, i.e., fullspace clusters, and the data objects have unique identifiers. These identifiers are used for tracking clusters by measuring the number of objects two clusters have in common, i.e. clusters are traced based on similar object sets. These conditions, however, are strict: First, in complex data, clusters are often hidden in individual subsets of the dimensions. Second, mapping clusters based on similar objects sets does not reflect the idea of tracing similar behavior types over time, because similar behavior can even be represented by clusters having no objects in common. A tracing method based on similar object values is needed. In this paper, we introduce a novel approach that traces subspace clusters based on object value similarity. Neither subspace tracing nor tracing by object value similarity has been done before.