Visualizing and exploring profiles with calling context ring charts

Calling context profiling is an important technique for analyzing the performance of object‐oriented software with complex inter‐procedural control flow. The Calling Context Tree (CCT) is a common data structure that stores dynamic metrics, such as CPU time, separately for each calling context. As CCTs may comprise millions of nodes, there is a need for a condensed visualization that eases the localization of performance bottlenecks. In this article, we discuss Calling Context Ring Charts (CCRCs), a compact visualization for CCTs, where callee methods are represented in ring segments surrounding the caller's ring segment. In order to reveal hot methods, their callers, and callees, the ring segments can be sized according to a chosen dynamic metric. We describe two case studies where CCRCs help us to detect and fix performance problems in applications. A performance evaluation also confirms that our implementation can efficiently handle large CCTs. Copyright © 2010 John Wiley & Sons, Ltd.     

<Emphasis Type="Italic">Smart CAPs for Smart Its</Emphasis> – Context Detection for Mobile Users

Context detection for mobile users plays a major role for enabling novel, human-centric interfaces. For this, we introduce a context detection scheme applicable in a self-organized sensor network, which is formed of disseminated, computer empowered sensors, referred to as Smart-Its [1]. Context-detection takes place without requiring any central point of control, and supports push as well as pull modes. Our solution is based on an in-network composition approach relying on so-called smart Context-Aware Packets (sCAPs). These packets act as a uniform interchange format, and allow single sensors to share sensed data and to cooperate to build up a meaningful context model from manifold inputs. sCAPs travel through the sensor network governed by an enclosed retrieving plan, specifying which sensors to visit in order to gain a specific piece of context information. For enhanced flexibility, the retrieving plan itself may be dynamically altered in accordance with past sensor readings.     

Modeling user context with applications to media retrieval

In this paper, we develop a theoretical understanding of multi-sensory knowledge and user context and their inter-relationships. This is used to develop a generic representation framework for multi-sensory knowledge and context. A representation framework for context can have a significant impact on media applications that dynamically adapt to user needs. There are three key contributions of this work: (a) theoretical analysis, (b) representation framework and (c) experimental validation. Knowledge is understood to be a dynamic set of multi-sensory facts with three key properties – multi-sensory, emergent and dynamic. Context is the dynamic subset of knowledge that affects the communication between entities. We develop a graph based, multi-relational representation framework for knowledge, and model its temporal dynamics using a linear dynamical system. Our approach results in a stable and convergent system. We applied our representation framework to a image retrieval system with a large collection of photographs from everyday events. Our experimental validation with the retrieval evaluated against two reference algorithms indicates that our context based approach provides significant gains in real-world usage scenarios.     

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.     

Model-driven specification and enforcement of RBAC break-glass policies for process-aware information systems

ContextIn many organizational environments critical tasks exist which – in exceptional cases such as an emergency – must be performed by a subject although he/she is usually not authorized to perform these tasks. Break-glass policies have been introduced as a sophisticated exception handling mechanism to resolve such situations. They enable certain subjects to break or override the standard access control policies of an information system in a controlled manner.ObjectiveIn the context of business process modeling a number of approaches exist that allow for the formal specification and modeling of process-related access control concepts. However, corresponding support for break-glass policies is still missing. In this paper, we aim at specifying a break-glass extension for process-related role-based access control (RBAC) models.MethodWe use model-driven development (MDD) techniques to provide an integrated, tool-supported approach for the definition and enforcement of break-glass policies in process-aware information systems. In particular, we provide modeling support on the computation independent model (CIM) layer as well as on the platform independent model (PIM) and platform specific model (PSM) layers.ResultsOur approach is generic in the sense that it can be used to extend process-aware information systems or process modeling languages with support for process-related RBAC and corresponding break-glass policies. Based on the formal CIM layer metamodel, we present a UML extension on the PIM layer that allows for the integrated modeling of processes and process-related break-glass policies via extended UML Activity diagrams. We evaluated our approach in a case study on real-world processes. Moreover, we implemented our approach at the PSM layer as an extension to the BusinessActivity library and runtime engine.ConclusionOur integrated modeling approach for process-related break-glass policies allows for specifying break-glass rules in process-aware information systems.     

Semantics for consistent activation in context-oriented systems

ContextContext-oriented programming languages provide dedicated programming abstractions to define behavioral adaptations and means to combine those adaptations dynamically according to sensed context changes. Some of these languages feature programming abstractions to explicitly define interaction dependencies among contexts. However, the semantics of context activation and the meaning of dependency relations have been described only informally, which in some cases has led to incorrect specifications, faulty implementations and inconsistent system behavior.ObjectiveWith the aim of avoiding faulty implementations and inconsistencies during system execution, this paper proposes both a formal and run-time model of contexts, context activation and context interaction.MethodAs a formal and computational basis, we introduce context Petri nets, a model based on Petri nets, which we found to match closely the structure of contexts in context-oriented systems. The operational semantics of Petri nets permits the modeling of run-time context activations. Existing Petri net analyses allow us to reason about system properties. As validation, we carried out small and medium-sized case studies.ResultsIn the cases explored, context Petri nets served effectively as underlying run-time model to ensure that declared context interaction constraints remain consistent during context manipulation. Moreover, context Petri nets enabled us to analyze certain properties regarding the activation state of particular contexts.ConclusionContext Petri nets thus proved to be appropriate to encode and manage the semantics of context activation, both formally and computationally, so as to preserve the consistency of context-oriented systems.     

A context-aware recommender method based on text and opinion mining

A recommender system is an information filtering technology that can be used to recommend items that may be of interest to users. Additionally, there are the context-aware recommender systems that consider contextual information to generate the recommendations. Reviews can provide relevant information that can be used by recommender systems, including contextual and opinion information. In a previous work, we proposed a context-aware recommendation method based on text mining (CARM-TM). The method includes two techniques to extract context from reviews: CIET.5_embed, a technique based on word embeddings; and RulesContext, a technique based on association rules. In this work, we have extended our previous method by including CEOM, a new technique which extracts context by using aspect-based opinions. We call our extension of CARM-TOM (context-aware recommendation method based on text and opinion mining). To generate recommendations, our method makes use of the CAMF algorithm, a context-aware recommender based on matrix factorization. To evaluate CARM-TOM, we ran an extensive set of experiments in a dataset about restaurants, comparing CARM-TOM against the MF algorithm, an uncontextual recommender system based on matrix factorization; and against a context extraction method proposed in literature. The empirical results strongly indicate that our method is able to improve a context-aware recommender system.     

A multicontext architecture for formalizing complex reasoning

We propose multicontext systems (MC systems) as a formal framework for the specification of complex reasoning. MC systems provide the ability to structure the specification of “global” reasoning in terms of “local” reasoning subpatterns. Each subpattern is modeled as a deduction in a context, formally defined as an axiomatic formal system. the global reasoning pattern is modeled as a concatenation of contextual deductions via bridge rules, i.e., inference rules that infer a fact in one context from facts asserted in other contexts. Besides the formal framework, in this article we propose a three-layer architecture designed to specify and automatize complex reasoning. At the first level we have object-level contexts (called s-contexts) for domain specifications. Problem-solving principles and, more in general, meta-level knowledge about the application domain is specified in a distinct context, called Problem-Solving Context (PSC). On top of s-contexts and PSC, we have a further context, called MT, where it is possible to specify strategies to control multicontext reasoning spanning through s-contexts and PSC. We show how GETFOL can be used as a computer tool for the implementation of MC systems and for the automatization of multicontext deductions. © 1995 John Wiley & Sons, Inc.     

Understanding and Using Context

Context is a poorly used source of information in our computing environments. As a result, we have an impoverished understanding of what context is and how it can be used. In this paper, we provide an operational definition of context and discuss the different ways in which context can be used by context-aware applications. We also present the Context Toolkit, an architecture that supports the building of these context-aware applications. We discuss the features and abstractions in the toolkit that make the task of building applications easier. Finally, we introduce a new abstraction, a situation which we believe will provide additional support to application designers.     

The aesthetic in mathematics as story

Abstract

Human cognition is story based. We think in terms of stories; we understand the world in terms of stories that we have already understood; we learn by living and accommodating new stories; and we define ourselves through the stories we tell ourselves. In this paper we conceptualize mathematical engagement as aesthetic and place it within the context of mathematics as story. We imagine mathematics teaching and learning experiences as stories acted out on an educational stage. We consider what types of teacher and student roles make good mathematical stories and make a classroom story worth living and discuss the roles the aesthetic may play within such stories. We contend that mathematics is an aesthetic and a storied experience. In this paper we explore the interplay between what is anaesthetic mathematics experience and what is a good mathematics story. We do this in the context of a mathematical applet, namely the Colour Calculator (Sinclair, 2001), and in the context of our research into the mathematical and pedagogical thinking of elementary mathematics teachers. In the end, we suggest that it is redundant to use both aesthetic and story to describe the mathematics experience, as stories are aesthetic in nature. First, however, we discuss what we mean by aesthetic and by story.     

Non-dominated differential context modeling for context-aware recommendations

Context plays an important role in the process of decision making. A user’s preferences on the items may vary from contexts to contexts, e.g., a user may prefer to watch a different type of the movies, if he or she is going to enjoy the movie with partner rather than with children. Context-aware recommender systems, therefore, were developed to adapt the recommendations to different contextual situations, such as time, location, companion, etc. Differential context modeling is a series of recommendation models which incorporate contextual hybrid filtering into the neighborhood based collaborative filtering approaches. In this paper, we propose to enhance differential context modeling by utilizing a non-dominated user neighborhood. The notion of dominance relation was originally proposed in multi-objective optimization, and it was reused to definite non-dominated user neighborhood in collaborative filtering recently. These non-dominated user neighbors refer to the neighbors that dominate others from different perspectives of the user similarities, such as the user-user similarities based on ratings, demographic information, social relationships, and so forth. In this paper, we propose to identify the non-dominated user neighborhood by exploiting user-user similarities over multiple contextual preferences. Our experimental results can demonstrate the effectiveness of the proposed approaches in comparison with popular context-aware collaborative filtering models over five real-world contextual rating data sets.

     

Reduction of the size of L-fuzzy contexts. A tool for differential diagnoses of diseases

ABSTRACT

Information extraction from an L-fuzzy context becomes a hard problem when we work with a large set of objects and/or attributes. The goal of this paper is to present two different and complementary techniques to reduce the size of the context. First, using overlap indexes, we will establish rankings among the elements of the context that will allow us to determine those that do not provide relevant information and eliminate them. Second, by means of Choquet integrals, we will aggregate some objects or attributes of the context in order to jointly use the provided information. One interesting application of the developed theory consists on helping in the differential diagnoses of diseases that share a large number of symptoms and, therefore, that are difficult of distinguish.     

A Novel Context-based Technique for Web Information Retrieval

In this paper we present context matching, a novel context-based technique for the ad-hoc retrieval of web documents. The aim of the technique is to dynamically generate a measure of document term significance during retrieval that can be used as a substitute or co-contributor of the term frequency measure. Unlike term frequency, which relies on a term occurring multiple times in a document to be considered significant, context matching is based on the notion that if a term in a given document occurs in that document in the context of the query, then that term is deemed to be significant. Context matching has the ability to potentially determine a term to be significant even if it occurs only once in a document. Vice versa, it also has the ability to determine a term to be insignificant, even if occurs frequently within a document. We show how expanded terms generated by a typical query expansion technique can be used effectively as query context for context matching. The technique is ideally suited to the nature of web information retrieval and we show how context matching significantly improves retrieval accuracy through experimental results on TREC web benchmark data.     

A Goal-Dependent Abstraction for Legal Reasoning by Analogy

This paper presents a new algorithm to find an appropriate similarityunder which we apply legal rules analogically. Since there may exist a lotof similarities between the premises of rule and a case in inquiry, we haveto select an appropriate similarity that is relevant to both thelegal rule and a top goal of our legal reasoning. For this purpose, a newcriterion to distinguish the appropriate similarities from the others isproposed and tested. The criterion is based on Goal-DependentAbstraction (GDA) to select a similarity such that an abstraction basedon the similarity never loses the necessary information to prove the ground (purpose of legislation) of the legal rule. In order to cope withour huge space of similarities, our GDA algorithm uses some constraintsto prune useless similarities.     

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。     

Discrete time control problems with uncertain information

Discrete-time control problems described by a state vectorx _i , a control vectoru _i , and a parameter vectorm _i are considered. Using an information spectrum, we define what is known and how to model uncertainties in our system. Depending on the information available to the control unit of the system, we consider feasibility, stability, optimality, and learning ability of such systems with incomplete information. For the special case of a deterministic, memoryless state-set feedback control law, we develop a criterion for global uniform asymptotic stability about the zero state and a dynamic programming approach to optimize processes with a finite time horizon. A technique for solving the generalized Bellman functional equations is outlined.     

An evaluation method of computer usability based on human-to-computer information transmission model

Abstract

This paper proposes a new evaluation and prediction method for computer usability. This method is based on our two previously proposed information transmission measures created from a human-to-computer information transmission model. The model has three information transmission levels: the device, software, and task content levels. Two measures, called the device independent information measure (DI) and the computer independent information measure (CI), defined on the software and task content levels respectively, are given as the amount of information transmitted. Two information transmission rates are defined as DI/T and CI/T, where T is the task completion time: the device independent information transmission rate (R _DI), and the computer independent information transmission rate (R _CI). The method utilizes the R _DI and R _CI rates to evaluate relatively the usability of software and device operations on different computer systems. Experiments using three different systems, in this case a graphical information input task, confirm that the method offers an efficient way of determining computer usability.     

面向跨媒体检索的层级循环注意力网络模型

目的 跨媒体检索旨在以任意媒体数据检索其他媒体的相关数据,实现图像、文本等不同媒体的语义互通和交叉检索。然而,"异构鸿沟"导致不同媒体数据的特征表示不一致,难以实现语义关联,使得跨媒体检索面临巨大挑战。而描述同一语义的不同媒体数据存在语义一致性,且数据内部蕴含着丰富的细粒度信息,为跨媒体关联学习提供了重要依据。现有方法仅仅考虑了不同媒体数据之间的成对关联,而忽略了数据内细粒度局部之间的上下文信息,无法充分挖掘跨媒体关联。针对上述问题,提出基于层级循环注意力网络的跨媒体检索方法。方法 首先提出媒体内-媒体间两级循环神经网络,其中底层网络分别建模不同媒体内部的细粒度上下文信息,顶层网络通过共享参数的方式挖掘不同媒体之间的上下文关联关系。然后提出基于注意力的跨媒体联合损失函数,通过学习媒体间联合注意力来挖掘更加精确的细粒度跨媒体关联,同时利用语义类别信息增强关联学习过程中的语义辨识能力,从而提升跨媒体检索的准确率。结果 在2个广泛使用的跨媒体数据集上,与10种现有方法进行实验对比,并采用平均准确率均值MAP作为评价指标。实验结果表明,本文方法在2个数据集上的MAP分别达到了0.469和0.575,超过了所有对比方法。结论 本文提出的层级循环注意力网络模型通过挖掘图像和文本的细粒度信息,能够充分学习图像和文本之间精确跨媒体关联关系,有效地提高了跨媒体检索的准确率。     

Scalable landmark recognition using EXTENT

We have proposed the EXTENT system for automated photograph annotation using image content and context analysis. A key component of EXTENT is a Landmark recognition system called LandMarker. In this paper, we present the architecture of LandMarker. The content of a query photograph is analyzed and compared against a database of sample landmark images, to recognize any landmarks it contains. An algorithm is presented for comparing a query image with a sample image. Context information may be used to assist landmark recognition. Also, we show how LandMarker deals with scalability to allow recognition of a large number of landmarks. We have implemented a prototype of the system, and present empirical results on a large dataset.

Good Samaritans on social network services: Effects of shared context information on social supports for strangers

Technological developments in smart phones and various sensors have been growing at a rapid pace, which leads people to share their context information with other users and support strangers in social network services (SNS). This allows us to study the effects of shared context information on socially supportive behaviors of individuals (Good Samaritans) in SNS. Our aim of this study is to investigate the pattern of social support amongst strangers in SNS. We aim to find out which types and forms of context information induce high levels of willingness to provide social support among strangers. We focus on four different types of context information (location, activity, emotion, or physical environment), which vary by degree of self-disclosure. We also investigated two forms of context information (subjective vs. objective), which differ depending on the provision of human interpretation.In order to achieve our research goals, we first constructed a causal model between context information and social support mediated by the theory of mind, which consists of simulation theory (empathy) and theory theory (rational reasoning). To verify the model, we conducted two exploratory pre-studies and a controlled main experiment. Our results indicated that types and forms of context information affect social support, simulation theory and theory theory. First, we found that a high level of self-disclosure positively effects social support. Context information in the subjective form induced more social supports compared to the objective form even though the information content itself is the same. Emotional context information presented in the subjective form has the strongest positive effect on social support. Furthermore, a high level of self-disclosure was found to positively affect context form's effect on simulation theory and social supports. In regard to theory theory, we discovered that high levels of self-disclosure have a positive effect.Both practical and theoretical implications of the study results have been presented. Theoretically, a conceptual model of the effects of context information on intention to provide social supports has been proposed and empirically verified. Practically, the interplay between context information types and forms can be utilized to construct social network services efficiently promoting social supports among fellow users. This paper ends with study limitations and implications.