The effects of helping,self-expression,and enjoyment on social capital in social media: the moderating effect of avoidance attachment in the tourism context

ABSTRACT

A majority of senior Internet users maintains persistent social connections with others through social media (SM), such as Facebook and Twitter. This research explores the impacts of helping, self-expression, and enjoyment on bonding social capital and bridging social capital which in turn influence SM involvement; it also explores the moderating effect of avoidance attachment among senior SM users. The results show that helping, self-expression, and enjoyment have significant impacts on bonding social capital and bridging social capital. Also, bonding social capital and bridging social capital have significant effects on SM involvement. Furthermore, relationships between helping and bonding social capital, between self-expression and bonding social capital, and between self-expression and bridging social capital are stronger for seniors with high avoidance attachment. Conversely, the relationships between enjoyment and bonding social capital as well as between enjoyment and bridging social capital are stronger for seniors with low avoidance attachment. Accordingly, the results of this study provide theoretical contributions to the literature of behaviour and information technology. In addition, some implications of the research and various strategies for practitioners and marketers that can be used to better increase users’ involvement in SM for tourism-related activities are discussed.     

渐进式前景更新和区域关联的图像协同分割

目的 图像协同分割技术是通过多幅参考图像以实现前景目标与背景区域的分离，并已被广泛应用于图像分类和目标识别等领域中。不过，现有多数的图像协同分割算法只适用于背景变化较大且前景几乎不变的环境。为此，提出一种新的无监督协同分割算法。方法 本文方法是无监督式的，在分级图像分割的基础上通过渐进式优化框架分别实现前景和背景模型的更新估计，同时结合图像内部和不同图像之间的分级区域相似度关联进一步增强上述模型估计的鲁棒性。该无监督的方法不需要进行预先样本学习，能够同时处理两幅或多幅图像且适用于同时存在多个前景目标的情况，并且能够较好地适应前景物体类的变化。结果 通过基于iCoseg和MSRC图像集的实验证明，该算法无需图像间具有显著的前景和背景差异这一约束，与现有的经典方法相比更适用于前景变化剧烈以及同时存在多个前景目标等更为一般化的图像场景中。结论 该方法通过对分级图像分割得到的超像素外观分布分别进行递归式估计来实现前景和背景的有效区分，并同时融合了图像内部以及不同图像区域之间的区域关联性来增加图像前景和背景分布估计的一致性。实验表明当前景变化显著时本文方法相比于现有方法具有更为鲁棒的表现。     

基于视频对象的视频内容分级描述模型

本文提出了一种基于视频对象的视频内容分级描述模型.视频序列首先被分成一个个的镜头,在每个镜头内对视频对象进行分割和跟踪.按照镜头、视频对象、视频对象平面和视频对象区域四级框架提取特征,对视频内容进行分级描述.本文对视频内容信息的描述可以用于视频检索、视频注释等应用.     

Maintenance of a Piercing Set for Intervals with Applications

   Abstract. We show how to maintain efficiently a minimum piercing set for a set S of intervals on the line, under insertions and deletions to/from S. A linear-size dynamic data structure is presented, which enables us to compute a new minimum piercing set following an insertion or deletion in time O(c( S) log |S|), where c (S) is the size of the new minimum piercing set. We also show how to maintain a piercing set for S of size at most (1+ɛ)c (S), for 0 < ɛ ≤ 1 , in

多媒体技术研究:2014——深度学习与媒体计算

目的 海量数据的快速增长给多媒体计算带来了深刻挑战。与传统以手工构造为核心的媒体计算模式不同,数据驱动下的深度学习(特征学习)方法成为当前媒体计算主流。方法 重点分析了深度学习在检索排序与标注、多模态检索与语义理解、视频分析与理解等媒体计算方面的最新进展和所面临的挑战,并对未来的发展趋势进行展望。结果 在检索排序与标注方面, 基于深度学习的神经编码等方法取得了很好的效果;在多模态检索与语义理解方面,深度学习被用于弥补不同模态间的“异构鸿沟“以及底层特征与高层语义间的”语义鸿沟“,基于深度学习的组合语义学习成为研究热点;在视频分析与理解方面, 深度神经网络被用于学习视频的有效表示方式及动作识别,并取得了很好的效果。然而,深度学习是一种数据驱动的方法,易受数据噪声影响, 对于在线增量学习方面还不成熟,如何将深度学习与众包计算相结合是一个值得期待的问题。结论 该综述在深入分析现有方法的基础上,对深度学习框架下为解决异构鸿沟和语义鸿沟给出新的思路。     

Synchronously tracking entities and relations in a syntax-aware parallel architecture for aspect-opinion pair extraction

Aspect-Opinion Pair Extraction (AOPE) task aims to capture each aspect with its corresponding opinions in user reviews. Entity recognition and relation detection are two fundamental subtasks of AOPE. Although recent works take interaction into account, the two subtasks are still relatively independent during calculation. Furthermore, since AOPE task has not been formally proposed for a long time, syntactic information does not attract much attention in the current deep learning models for AOPE. In this paper, we propose a model for Synchronously Tracking Entities and Relations (STER) to deal with AOPE. Specifically, we design a network consisting of a bank of gated RNNs, where we can track all entities of a review sentence in parallel. STER utilizes three features, i.e., context, syntax and relation, to learn the representation of each tracked entity and calculate the correlated degree between all entities synchronously at each time step. The entity representation and the correlated degree are highly dependent during calculation. Finally, they will be used for entity recognition and relation detection, respectively. Therefore, in STER, the two subtasks of AOPE can achieve sufficient interaction, which enhances their mutual heuristic effect heavily. To verify the effectiveness and adaptiveness of our model, we conduct experiments on two annotation versions of SemEval datasets. The results demonstrate that STER not only achieves advanced performances but adapts to different annotation strategies well.

     

An Observation Approach to the Semantics of Agent Communication Languages

We show how a formal framework for the observation issue in computer systems can be used for the specification of an agent behavior, abstracting away from agent inner details while focusing on its interactive behavior. This model can also be used as a specification of agent communication languages (ACLs), providing the proper abstraction level to represent the conditions causing an agent to send a message, as well as its effect on the receiving agent. In particular, this approach generalizes upon existing ACL semantics, such as FIPA ACL, that relate agent communicative acts to the agent mental state. Since the observation framework induces a more abstract architecture than other known approaches, our semantics are likely to be applicable to a wider set of agent architectures, thus better supporting standardization aims. Some application examples are shown, describing how various aspects of ACL semantics can be specified within our framework.     

MSR4SM: Using topic models to effectively mining software repositories for software maintenance tasks

ContextMining software repositories has emerged as a research direction over the past decade, achieving substantial success in both research and practice to support various software maintenance tasks. Software repositories include bug repository, communication archives, source control repository, etc. When using these repositories to support software maintenance, inclusion of irrelevant information in each repository can lead to decreased effectiveness or even wrong results.ObjectiveThis article aims at selecting the relevant information from each of the repositories to improve effectiveness of software maintenance tasks.MethodFor a maintenance task at hand, maintainers need to implement the maintenance request on the current system. In this article, we propose an approach, MSR4SM, to extract the relevant information from each software repository based on the maintenance request and the current system. That is, if the information in a software repository is relevant to either the maintenance request or the current system, this information should be included to perform the current maintenance task. MSR4SM uses the topic model to extract the topics from these software repositories. Then, relevant information in each software repository is extracted based on the topics.ResultsMSR4SM is evaluated for two software maintenance tasks, feature location and change impact analysis, which are based on four subject systems, namely jEdit, ArgoUML, Rhino and KOffice. The empirical results show that the effectiveness of traditional software repositories based maintenance tasks can be greatly improved by MSR4SM.ConclusionsThere is a lot of irrelevant information in software repositories. Before we use them to implement a maintenance task at hand, we need to preprocess them. Then, the effectiveness of the software maintenance tasks can be improved.     

A formal framework for software product lines

ContextA Software Product Line is a set of software systems that are built from a common set of features. These systems are developed in a prescribed way and they can be adapted to fit the needs of customers. Feature models specify the properties of the systems that are meaningful to customers. A semantics that models the feature level has the potential to support the automatic analysis of entire software product lines.ObjectiveThe objective of this paper is to define a formal framework for Software Product Lines. This framework needs to be general enough to provide a formal semantics for existing frameworks like FODA (Feature Oriented Domain Analysis), but also to be easily adaptable to new problems.MethodWe define an algebraic language, called SPLA, to describe Software Product Lines. We provide the semantics for the algebra in three different ways. The approach followed to give the semantics is inspired by the semantics of process algebras. First we define an operational semantics, next a denotational semantics, and finally an axiomatic semantics. We also have defined a representation of the algebra into propositional logic.ResultsWe prove that the three semantics are equivalent. We also show how FODA diagrams can be automatically translated into SPLA. Furthermore, we have developed our tool, called AT, that implements the formal framework presented in this paper. This tool uses a SAT-solver to check the satisfiability of an SPL.ConclusionThis paper defines a general formal framework for software product lines. We have defined three different semantics that are equivalent; this means that depending on the context we can choose the most convenient approach: operational, denotational or axiomatic. The framework is flexible enough because it is closely related to process algebras. Process algebras are a well-known paradigm for which many extensions have been defined.     

Ensembles and locality: Insight on improving software effort estimation

ContextEnsembles of learning machines and locality are considered two important topics for the next research frontier on Software Effort Estimation (SEE).ObjectivesWe aim at (1) evaluating whether existing automated ensembles of learning machines generally improve SEEs given by single learning machines and which of them would be more useful; (2) analysing the adequacy of different locality approaches; and getting insight on (3) how to improve SEE and (4) how to evaluate/choose machine learning (ML) models for SEE.MethodA principled experimental framework is used for the analysis and to provide insights that are not based simply on intuition or speculation. A comprehensive experimental study of several automated ensembles, single learning machines and locality approaches, which present features potentially beneficial for SEE, is performed. Additionally, an analysis of feature selection and regression trees (RTs), and an investigation of two tailored forms of combining ensembles and locality are performed to provide further insight on improving SEE.ResultsBagging ensembles of RTs show to perform well, being highly ranked in terms of performance across different data sets, being frequently among the best approaches for each data set and rarely performing considerably worse than the best approach for any data set. They are recommended over other learning machines should an organisation have no resources to perform experiments to chose a model. Even though RTs have been shown to be more reliable locality approaches, other approaches such as k-Means and k-Nearest Neighbours can also perform well, in particular for more heterogeneous data sets.ConclusionCombining the power of automated ensembles and locality can lead to competitive results in SEE. By analysing such approaches, we provide several insights that can be used by future research in the area.     

Geographic ‘Place’ and ‘Community Information’ Preferences

People dynamically structure social interactions and activities at various locations in their environments in specialized types of places such as the office, home, coffee shop, museum and school. They also imbue various locations with personal meaning, creating group ‘hangouts’ and personally meaningful ‘places’. Mobile location-aware community systems can potentially utilize the existence of such ‘places’ to support the management of social information and interaction. However, acting effectively on this potential requires an understanding of how: (1) places and place-types relate to people’s desire for place-related awareness of and communication with others; and (2) what information people are willing to provide about themselves to enable place-related communication and awareness. We present here the findings from two qualitative studies, a survey of 509 individuals in New York, and a study of how mobility traces can be used to find people’s important places in an exploration of these questions. These studies highlight how people value and are willing to routinely provide information such as ratings, comments, event records relevant to a place, and when appropriate their location to enable services. They also suggest how place and place-type data could be used in conjunction with other information regarding people and places so that systems can be deployed that respect users’ People-to-People-to-Places data sharing preferences. We conclude with a discussion on how ‘place’ data can best be utilized to enable services when the systems in question are supported by a sophisticated computerized user-community social-geographical model.     

Civil liability for misuse of online communication through websites: an analytical study of UAE law

ABSTRACT

This paper takes the UAE legal system as case in point to illustrate certain challenges posed for jurists by the spread of the Internet. Since it enables large-scale participatory multimedia content production, the Internet is surrounded by regulatory safeguards for ensuring its public accountability. In the UAE, the National Media Council holds responsibility for licensing media outlets, including online ones, and for overseeing content published therein. Together, legal disciplines on content, regulatory accountability frameworks and the collaborative nature of Internet content production form the context in which existing regimes, such as for civil liability (tort), call for interpretive adaptation. This is what this paper undertakes, with respect to civil liability claims for misuse of online communication through websites. A close analytical study of UAE law helps clarify how civil liability is established and apportioned between the National Media Council (vicarious liability), media outlets and individual users, alongside other findings.     

Cross-language transfer of semantic annotation via targeted crowdsourcing: task design and evaluation

Modern data-driven spoken language systems (SLS) require manual semantic annotation for training spoken language understanding parsers. Multilingual porting of SLS demands significant manual effort and language resources, as this manual annotation has to be replicated. Crowdsourcing is an accessible and cost-effective alternative to traditional methods of collecting and annotating data. The application of crowdsourcing to simple tasks has been well investigated. However, complex tasks, like cross-language semantic annotation transfer, may generate low judgment agreement and/or poor performance. The most serious issue in cross-language porting is the absence of reference annotations in the target language; thus, crowd quality control and the evaluation of the collected annotations is difficult. In this paper we investigate targeted crowdsourcing for semantic annotation transfer that delegates to crowds a complex task such as segmenting and labeling of concepts taken from a domain ontology; and evaluation using source language annotation. To test the applicability and effectiveness of the crowdsourced annotation transfer we have considered the case of close and distant language pairs: Italian–Spanish and Italian–Greek. The corpora annotated via crowdsourcing are evaluated against source and target language expert annotations. We demonstrate that the two evaluation references (source and target) highly correlate with each other; thus, drastically reduce the need for the target language reference annotations.     

Design thinking and imitatio in an educational setting

Schools are expected to prepare students for the future, providing them with methods for dealing with the emergent world. This article considers how teachers can work with digital productions at primary schools even when they are not acquainted with the new production genres. We propose a methodological framework to assist students and teachers in their exploration of unknown media genres based on a case study. We revive the ancient concept of imitatio and integrate it with contemporary design thinking to support production dynamics that lead to increased digital literacy.     

Syncretistic images: iPhone fiction filmmaking and its cognitive ramifications

Abstract

This article will address the question of how fiction films are individuated in terms of image quality on the grounds of the recording technology used. As new cost-effective digital recording technologies are introduced to the marketplace, this becomes a salient issue to understand for producers and production teams. In order to define cameras’ image quality capacities, three almost identical short fiction film sequences were tested on a young audience in a comparative blind test. Surprisingly, the result unambiguously showed that most viewers preferred the film recorded on an iPhone. Based on Barbara Maria Stafford's theoretical framework on the cognitive work of images and theories that concern ecological moving image theory, the analysis of this article aims to explain the reception study's result by illuminating the sublime and ambiguous figure–ground constellation of the iPhone video and its cognitive ramifications.