Automatically Annotating Structured Web Data Using a SVM-Based Multiclass Classifier

In this paper, we propose a new learning approach to Web data annotation, where a support vector machine-based multiclass classifier is trained to assign labels to data items. For data record extraction, a data section re-segmentation algorithm based on visual and content features is introduced to improve the performance of Web data record extraction. We have implemented the proposed approach and tested it with a large set of Web query result pages in different domains. Our experimental results show that our proposed approach is highly effective and efficient.

Practical Spectrum Aggregation for Secondary Networks with Imperfect Sensing

We investigate a collision-sensitive secondary network that intends to opportunistically aggregate and utilize spectrum of a primary network to achieve higher data rates. In opportunistic spectrum access with imperfect sensing of idle primary spectrum, secondary transmission can collide with primary transmission. When the secondary network aggregates more channels in the presence of the imperfect sensing, collisions could occur more often, limiting the performance obtained by spectrum aggregation. In this context, we aim to address a fundamental query, that is, how much spectrum aggregation is worthy with imperfect sensing. For collision occurrence, we focus on two different types of collision: one is imposed by asynchronous transmission; and the other by imperfect spectrum sensing. The collision probability expression has been derived in closed-form with various secondary network parameters: primary traffic load, secondary user transmission parameters, spectrum sensing errors, and the number of aggregated sub-channels. In addition, the impact of spectrum aggregation on data rate is analysed under the constraint of collision probability. Then, we solve an optimal spectrum aggregation problem and propose the dynamic spectrum aggregation approach to increase the data rate subject to practical collision constraints. Our simulation results show clearly that the proposed approach outperforms the benchmark that passively aggregates sub-channels with lack of collision awareness.

Retrieving Regions of Interest for User Exploration

We consider an application scenario where points of interest (PoIs) each have a web presence and where a web user wants to iden- tify a region that contains relevant PoIs that are relevant to a set of keywords, e.g., in preparation for deciding where to go to conve- niently explore the PoIs. Motivated by this, we propose the length- constrained maximum-sum region (LCMSR) query that returns a spatial-network region that is located within a general region of in- terest, that does not exceed a given size constraint, and that best matches query keywords. Such a query maximizes the total weight of the PoIs in it w.r.t. the query keywords. We show that it is NP- hard to answer this query. We develop an approximation algorithm with a (5 + ǫ) approximation ratio utilizing a technique that scales node weights into integers. We also propose a more efficient heuris- tic algorithm and a greedy algorithm. Empirical studies on real data offer detailed insight into the accuracy of the proposed algorithms and show that the proposed algorithms are capable of computingresults efficiently and effectively.

A Survey of Security in Software Defined Networks

The proposition of increased innovation in network applications and reduced cost for network operators has won over the networking world to the vision of Software-Defined Networking (SDN). With the excitement of holistic visibility across the network and the ability to program network devices, developers have rushed to present a range of new SDN-compliant hardware, software and services. However, amidst this frenzy of activity, one key element has only recently entered the debate: Network Security. In this article, security in SDN is surveyed presenting both the research community and industry advances in this area. The challenges to securing the network from the persistent attacker are discussed and the holistic approach to the security architecture that is required for SDN is described. Future research directions that will be key to providing network security in SDN are identified.

SDN Security: A Survey

The pull of Software-Defined Networking (SDN) is magnetic. There are few in the networking community who have escaped its impact. As the benefits of network visibility and network device programmability are discussed, the question could be asked as to who exactly will benefit? Will it be the network operator or will it, in fact, be the network intruder? As SDN devices and systems hit the market, security in SDN must be raised on the agenda. This paper presents a comprehensive survey of the research relating to security in software-defined networking that has been carried out to date. Both the security enhancements to be derived from using the SDN framework and the security challenges introduced by the framework are discussed. By categorizing the existing work, a set of conclusions and proposals for future research directions are presented.

European urban spaces in crisis - the mapping of affective pracitices with Living with Difference: introduction

The introduction outlines the notion of urban space and crisis in Europe while taking into account the more recent protests and riots in different cities, in and beyond Europe. It is argued that the phenomen of protest is happening alongside the economic crisis underscoring an alternative political public civic spirit expressing to a certain degree the renaissance and timely making of, what might be called in the digital age, #œuvre. Its forces and emotional properties capture a political realm that unfolds as a globalized urban transnational public space, still progressing. Further, it introduces the collection of papers for the special themed feature. Five papers look at affective practices through a Continental European lens, which places the meaning of race, migration and intersecting identity angles at the centre of debates of individual encounters in public spaces. The final and sixth paper, written by Brenda Yeoh, looks through a Singapore/East Asia lens, and comments on the common European threats as well as on the historical specificity and implications of distinctive geo-political spaces for affective practices.

Seeking the New Normal? Troubled Spaces of Encountering Visible Differences in Warsaw

In times of globalisation and super-mobility, ideas of normality are in turmoil. In different societies in, across and beyond Europe, we face the challenge of undoing specific notions of normality and creating more inclusive societies with an open culture of learning to live with differences. The scope of
the paper is to introduce some findings on encounters with difference and negotiations of social values in relation to a growing visibility of difference after 1989 in Poland, on the background of a critique of normality/normalisation and normalcy.On the basis of interviews conducted inWarsaw, we investigate how normality/normalisation discourses of visible homosexuality and physical disability are incorporated into individual self-reflections and justifications of prejudices (homophobia and disabilism). More specifically we argue that there are moments of ‘cultural transgressions’ present in everyday practices towards ‘visible’sexual and (dis)ability difference.

Efficient Processing of Spatial Group Keyword Queries

With the proliferation of geo-positioning and geo-tagging techniques, spatio-textual objects that possess both a geographical location and a textual description are gaining in prevalence, and spatial keyword queries that exploit both location and textual description are gaining in prominence. However, the queries studied so far generally focus on finding individual objects that each satisfy a query rather than finding groups of objects where the objects in a group together satisfy a query.

We define the problem of retrieving a group of spatio-textual objects such that the group's keywords cover the query's keywords and such that the objects are nearest to the query location and have the smallest inter-object distances. Specifically, we study three instantiations of this problem, all of which are NP-hard. We devise exact solutions as well as approximate solutions with provable approximation bounds to the problems. In addition, we solve the problems of retrieving top-k groups of three instantiations, and study a weighted version of the problem that incorporates object weights. We present empirical studies that offer insight into the efficiency of the solutions, as well as the accuracy of the approximate solutions.

Temporal Spatial-Keyword Top-k publish/subscribe

Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale. These geo-textual data cover a wide range of topics. Users are interested in receiving up-to-date tweets such that their locations are close to a user specified location and their texts are interesting to users. For example, a user may want to be updated with tweets near her home on the topic “food poisoning vomiting.” We consider the Temporal Spatial-Keyword Top-k Subscription (TaSK) query. Given a TaSK query, we continuously maintain up-to-date top-k most relevant results over a stream of geo-textual objects (e.g., geo-tagged Tweets) for the query. The TaSK query takes into account text relevance, spatial proximity, and recency of geo-textual objects in evaluating its relevance with a geo-textual object. We propose a novel solution to efficiently process a large number of TaSK queries over a stream of geotextual objects. We evaluate the efficiency of our approach on two real-world datasets and the experimental results show that our solution is able to achieve a reduction of the processing time by 70-80% compared with two baselines.

Factors that impact on the use of mechanical ventilation weaning protocols in critically ill adults and children: a qualitative-evidence synthesis

Background:
Prolonged mechanical ventilation is associated with a longer intensive care unit (ICU) length of stay and higher mortality. Consequently, methods to improve ventilator weaning processes have been sought. Two recent Cochrane systematic reviews in ICU adult and paediatric populations concluded that protocols can be effective in reducing the duration of mechanical ventilation, but there was significant heterogeneity in study findings. Growing awareness of the benefits of understanding the contextual factors impacting on effectiveness has encouraged the integration of qualitative evidence syntheses with effectiveness reviews, which has delivered important insights into the reasons underpinning (differential) effectiveness of healthcare interventions.

Objectives:
1. To locate, appraise and synthesize qualitative evidence concerning the barriers and facilitators of the use of protocols for weaning critically-ill adults and children from mechanical ventilation;

2. To integrate this synthesis with two Cochrane effectiveness reviews of protocolized weaning to help explain observed heterogeneity by identifying contextual factors that impact on the use of protocols for weaning critically-ill adults and children from mechanical ventilation;

3. To use the integrated body of evidence to suggest the circumstances in which weaning protocols are most likely to be used.

Search methods:
We used a range of search terms identified with the help of the SPICE (Setting, Perspective, Intervention, Comparison, Evaluation) mnemonic. Where available, we used appropriate methodological filters for specific databases. We searched the following databases: Ovid MEDLINE, Embase, OVID, PsycINFO, CINAHL Plus, EBSCOHost, Web of Science Core Collection, ASSIA, IBSS, Sociological Abstracts, ProQuest and LILACS on the 26th February 2015. In addition, we searched: the grey literature; the websites of professional associations for relevant publications; and the reference lists of all publications reviewed. We also contacted authors of the trials included in the effectiveness reviews as well as of studies (potentially) included in the qualitative synthesis, conducted citation searches of the publications reporting these studies, and contacted content experts.

We reran the search on 3rd July 2016 and found three studies, which are awaiting classification.

Selection criteria:
We included qualitative studies that described: the circumstances in which protocols are designed, implemented or used, or both, and the views and experiences of healthcare professionals either involved in the design, implementation or use of weaning protocols or involved in the weaning of critically-ill adults and children from mechanical ventilation not using protocols. We included studies that: reflected on any aspect of the use of protocols, explored contextual factors relevant to the development, implementation or use of weaning protocols, and reported contextual phenomena and outcomes identified as relevant to the effectiveness of protocolized weaning from mechanical ventilation.

Data collection and analysis:
At each stage, two review authors undertook designated tasks, with the results shared amongst the wider team for discussion and final development. We independently reviewed all retrieved titles, abstracts and full papers for inclusion, and independently extracted selected data from included studies. We used the findings of the included studies to develop a new set of analytic themes focused on the barriers and facilitators to the use of protocols, and further refined them to produce a set of summary statements. We used the Confidence in the Evidence from Reviews of Qualitative Research (CERQual) framework to arrive at a final assessment of the overall confidence of the evidence used in the synthesis. We included all studies but undertook two sensitivity analyses to determine how the removal of certain bodies of evidence impacted on the content and confidence of the synthesis. We deployed a logic model to integrate the findings of the qualitative evidence synthesis with those of the Cochrane effectiveness reviews.

Main results:
We included 11 studies in our synthesis, involving 267 participants (one study did not report the number of participants). Five more studies are awaiting classification and will be dealt with when we update the review.

The quality of the evidence was mixed; of the 35 summary statements, we assessed 17 as ‘low’, 13 as ‘moderate’ and five as ‘high’ confidence. Our synthesis produced nine analytical themes, which report potential barriers and facilitators to the use of protocols. The themes are: the need for continual staff training and development; clinical experience as this promotes felt and perceived competence and confidence to wean; the vulnerability of weaning to disparate interprofessional working; an understanding of protocols as militating against a necessary proactivity in clinical practice; perceived nursing scope of practice and professional risk; ICU structure and processes of care; the ability of protocols to act as a prompt for shared care and consistency in weaning practice; maximizing the use of protocols through visibility and ease of implementation; and the ability of protocols to act as a framework for communication with parents.

Authors' conclusions:
There is a clear need for weaning protocols to take account of the social and cultural environment in which they are to be implemented. Irrespective of its inherent strengths, a protocol will not be used if it does not accommodate these complexities. In terms of protocol development, comprehensive interprofessional input will help to ensure broad-based understanding and a sense of ‘ownership’. In terms of implementation, all relevant ICU staff will benefit from general weaning as well as protocol-specific training; not only will this help secure a relevant clinical knowledge base and operational understanding, but will also demonstrate to others that this knowledge and understanding is in place. In order to maximize relevance and acceptability, protocols should be designed with the patient profile and requirements of the target ICU in mind. Predictably, an under-resourced ICU will impact adversely on protocol implementation, as staff will prioritize management of acutely deteriorating and critically-ill patients.

YSOVAR: Mid-infrared Variability in the Star-forming Region Lynds 1688

The emission from young stellar objects (YSOs) in the mid-infrared (mid-IR) is dominated by the inner rim of their circumstellar disks. We present IR data from the Young Stellar Object VARiability (YSOVAR) survey of ~800 objects in the direction of the Lynds 1688 (L1688) star-forming region over four visibility windows spanning 1.6 yr using the Spitzer Space Telescope in its warm mission phase. Among all light curves, 57 sources are cluster members identified based on their spectral energy distribution and X-ray emission. Almost all cluster members show significant variability. The amplitude of the variability is larger in more embedded YSOs. Ten out of 57 cluster members have periodic variations in the light curves with periods typically between three and seven days, but even for those sources, significant variability in addition to the periodic signal can be seen. No period is stable over 1.6 yr. Nonperiodic light curves often still show a preferred timescale of variability that is longer for more embedded sources. About half of all sources exhibit redder colors in a fainter state. This is compatible with time-variable absorption toward the YSO. The other half becomes bluer when fainter. These colors can only be explained with significant changes in the structure of the inner disk. No relation between mid-IR variability and stellar effective temperature or X-ray spectrum is found.

Efficient RkNN Retrieval with Arbitrary Non-Metric Similarity Measures

A RkNN query returns all objects whose nearest k neighbors
contain the query object. In this paper, we consider RkNN
query processing in the case where the distances between
attribute values are not necessarily metric. Dissimilarities
between objects could then be a monotonic aggregate of dissimilarities
between their values, such aggregation functions
being specified at query time. We outline real world cases
that motivate RkNN processing in such scenarios. We consider
the AL-Tree index and its applicability in RkNN query
processing. We develop an approach that exploits the group
level reasoning enabled by the AL-Tree in RkNN processing.
We evaluate our approach against a Naive approach
that performs sequential scans on contiguous data and an
improved block-based approach that we provide. We use
real-world datasets and synthetic data with varying characteristics
for our experiments. This extensive empirical
evaluation shows that our approach is better than existing
methods in terms of computational and disk access costs,
leading to significantly better response times.

Fast Rule Mining over Multi-dimensional Windows

Association rule mining is an indispensable tool for discovering
insights from large databases and data warehouses.
The data in a warehouse being multi-dimensional, it is often
useful to mine rules over subsets of data defined by selections
over the dimensions. Such interactive rule mining
over multi-dimensional query windows is difficult since rule
mining is computationally expensive. Current methods using
pre-computation of frequent itemsets require counting
of some itemsets by revisiting the transaction database at
query time, which is very expensive. We develop a method
(RMW) that identifies the minimal set of itemsets to compute
and store for each cell, so that rule mining over any
query window may be performed without going back to the
transaction database. We give formal proofs that the set of
itemsets chosen by RMW is sufficient to answer any query
and also prove that it is the optimal set to be computed
for 1 dimensional queries. We demonstrate through an extensive
empirical evaluation that RMW achieves extremely
fast query response time compared to existing methods, with
only moderate overhead in pre-computation and storage

Fast Mining of Interesting Phrases from Subsets of Text Corpora

We address the problem of mining interesting phrases from subsets of a text corpus where the subset is specified using a set of features such as keywords that form a query. Previous algorithms for the problem have proposed solutions that involve sifting through a phrase dictionary based index or a document-based index where the solution is linear in either the phrase dictionary size or the size of the document subset. We propose the usage of an independence assumption between query keywords given the top correlated phrases, wherein the pre-processing could be reduced to discovering phrases from among the top phrases per each feature in the query. We then outline an indexing mechanism where per-keyword phrase lists are stored either in disk or memory, so that popular aggregation algorithms such as No Random Access and Sort-merge Join may be adapted to do the scoring at real-time to identify the top interesting phrases. Though such an approach is expected to be approximate, we empirically illustrate that very high accuracies (of over 90%) are achieved against the results of exact algorithms. Due to the simplified list-aggregation, we are also able to provide response times that are orders of magnitude better than state-of-the-art algorithms. Interestingly, our disk-based approach outperforms the in-memory baselines by up to hundred times and sometimes more, confirming the superiority of the proposed method.