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International audience ; assess the implementation and effectiveness of various interventions in order to identify possible success or failure factors. Methods: Comprehensive Systematic review was performed. After three steps data searching (in PubMed, SCOPUS, Cochrane, PsycINFO, Google Scholar) and critical appraisal, 63 studies out of 739 were fully retrieved. Results: Implementation aspects as intervention types (single or combined targeted risk factors), type of audience (students, parents, school staff, communities), settings (home, school or outdoor), type of organization (face to face, groups, online), professional or peer-led, communication type (written materials as brochures, posters or videoconferencing) or effectiveness measures were extracted and synthesized. Important features of context such as funding, European or government programs were used for clustering the studies. Conclusions: Health literacy interventions in adolescents need the involvement of education staff and parents participation. Health literacy for adolescents has to take into account cultural context, language, psychological features of each age; either digital or non-digital interventions cannot elude face to face communication between adolescents and their parents, academics or peers. Key messages: Health literacy school-based interventions are effective but the content for adolescents audience should rely on guidelines provided by interdisciplinary teams of experts. Health Literacy interventions in adolescents should be age-appropriate and do-not-harm precautions must always be taken in order to avoid prejudices, body-image or self-esteem concerns.

International audience ; assess the implementation and effectiveness of various interventions in order to identify possible success or failure factors. Methods: Comprehensive Systematic review was performed. After three steps data searching (in PubMed, SCOPUS, Cochrane, PsycINFO, Google Scholar) and critical appraisal, 63 studies out of 739 were fully retrieved. Results: Implementation aspects as intervention types (single or combined targeted risk factors), type of audience (students, parents, school staff, communities), settings (home, school or outdoor), type of organization (face to face, groups, online), professional or peer-led, communication type (written materials as brochures, posters or videoconferencing) or effectiveness measures were extracted and synthesized. Important features of context such as funding, European or government programs were used for clustering the studies. Conclusions: Health literacy interventions in adolescents need the involvement of education staff and parents participation. Health literacy for adolescents has to take into account cultural context, language, psychological features of each age; either digital or non-digital interventions cannot elude face to face communication between adolescents and their parents, academics or peers. Key messages: Health literacy school-based interventions are effective but the content for adolescents audience should rely on guidelines provided by interdisciplinary teams of experts. Health Literacy interventions in adolescents should be age-appropriate and do-not-harm precautions must always be taken in order to avoid prejudices, body-image or self-esteem concerns.

Political science data often contain grouped observations, which produces unobserved "cluster effects" in statistical models. Typical solutions include (1) ignoring the impact on coefficients and only adjusting the standard errors of generalized linear models (GLM) or (2) addressing clustering in coefficient estimation while relying on a parametric assumption for the cluster effects and/or a large number of clusters for standard errors. I show that both approaches are problematic for inference. Through simulation I demonstrate that multilevel modeling (MLM) and generalized estimating equations (GEE) produce more efficient coefficients than does GLM. Next, I show that commonly-used MLM and GEE standard error methods can be biased downward, while bootstrapping by resampling clusters (BCSE) performs better, even with a misspecified error distribution and/or few clusters. I recommend the use of MLM or GEE to estimate coefficients and BCSE to estimate uncertainty, and show that this approach can produce divergent conclusions in applied research.

ObjectivesClustering algorithms are commonly used to identify disease clusters. New Clustering algorithms are benchmarked on synthetic data to assess their accuracy. These datasets lack the complexities of real electronic health record (EHR) producing a partial assessment of the algorithm. We developed a synthetic EHR cluster generator for benchmarking clustering algorithms.
ApproachWe have created a synthetic EHR cluster generator, clustEHR, based on Synthea, a synthetic EHR generator that produces datasets (with parameterized noise and cluster separation) of known clusters and with clinically relevant patient outcomes. We evaluated clustEHR by generating multiple datasets of variable cluster separation and percentage of noise variables to reflect easy and hard clustering problems. We used a linear model to assess the relationship between these parameters and cluster problem difficulty. K-means accuracy was used as a proxy to measure cluster problem difficulty.
ResultsWe have developed a tool for generating synthetic EHR cluster data with clinically relevant outcomes based on the rate of decline of medical observations (e.g. blood pressure). The following parameters are supported: a) number of clusters, b) number of patients in each cluster, c) number and data type of features, d) separation through defining clusters as either diseases such as COPD or dementia (high separability) or inter-disease conditions such as emphysema and chronic bronchitis within COPD (low separability), and e) noise variables through identifying variables not predictive of true cluster outcomes random forest feature importance metric. We show that high cluster separation significantly increases k-means accuracy (coefficient of 0.33). Smaller percent of noise variables increase accuracy though not significantly (coefficient 0.42).
ConclusionClustEHR offers realistic mixed data types as well as outcomes which are frequently used to evaluate clusters when subtyping diseases. The evaluating results suggest that the difficulty of the cluster data can be user determined. The tool can be used to create realistic datasets for evaluating clustering approaches.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org ; "© Author(s), {2020}. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published inProceedings of the 36th Annual ACM Symposium on Applied Computing, http://dx.doi.org/10.1145/3412841.3442081 ; Addressing information overload in current e-participation platforms, we present a lightweight web application consisting of a simple HTML-based data panel that, through the use of date, location and category based filters, and several interactive graphs, allows visualizing, exploring and analyzing data obtained from public deliberation platforms in an easy and clear way. The tool, which implements natural language processing, text similarity, and graph clustering techniques to group citizen proposals, may serve as a decision support system for the municipal government, and may contribute to increase the citizens' participation and engagement ; This work was conducted with financial support from the Spanish Ministry of Science and Innovation (PID2019-108965GB-I00) and the Centre of Andalusian Studies (PR137/19)

Purpose
The purpose of this paper is to derive crucial insights from multi-scale analysis to detect equity return co-movements between Korean and emerging Asian markets.


Design/methodology/approach
Wavelet correlation, wavelet coherence and wavelet clustering measures are used to uncover Korean equity market interactions which are hard to see using any other modern econometric method and which would otherwise had remained hidden.


Findings
The authors observed that Korean equity market is strongly integrated with Asian equity markets at lower frequency scales and has a relatively weak correlation at higher frequencies. Further this correlation eventually grows strong in the interim of crises period at lower frequency scales. The authors, however, do not found any significant deviation in dendrograms generated in data clustering process from wavelet scale 2 to 6 which are associated with four and 64 weeks period, respectively. Overall the findings are relevant and have strong policy and practical implications.


Originality/value
The unique contribution of this paper is that it introduces wavelet clustering analysis to produce a nested hierarchy of similar markets at each frequency level for the first time in finance literature

Indonesia, as a country with great potential, should become an active Global Software Development (GSD) player. This study aims to identify and rank software engineers' competencies when working in a GSD environment. Having gathered competencies with a literature study, we then compiled the hierarchy of competencies, questionnaires and data processing using the Analytic Hierarchy Process. We involved Indonesia's experts in giving a portrait of the potential for global software engineers in Indonesia. From the research results, we obtained 40 competencies along with their ranking and clustering. We conclude that software engineers must master both hard and soft competencies by paying attention to the ranking and clustering generated in this study. This research is useful for practitioners, the Indonesian government and other countries to prioritise software engineer competencies. Further, it is useful for developed countries, as GSD clients, to see a portrait of software engineers' competence in Indonesia.

VERTEX COVER BASED LINK MONITORING TECHNIQUES FOR WIRELESS SENSOR NETWORKSAbstractWireless sensor networks (WSNs) are generally composed of numerous battery-powered tiny nodes that can sense from the environment and send this data through wireless communication. WSNs have wide range of application areas such as military surveillance, healthcare, miner safety, and outer space exploration. Inherent security weaknesses of wireless communication may prone WSNs to various attacks such as eavesdropping, jamming and spoofing. This situation attracts researchers to study countermeasures for detection and prevention of these attacks. Graph theory provides a very useful theoretical basis for solving WSN problems related to communication and security issues. One of the important graph theoretic structures is vertex cover (VC) in which a set of nodes are selected to cover the edges of the graph where each edge is incident to at least one node in VC set. Finding VC set having the minimum cardinality for a given graph is an NP-hard problem. In this paper, we describe VC algorithms aiming link monitoring where nodes in VC are configured as secure points. We investigate variants of VC problems such as weight and capacity constrained versions on different graph types to meet the energy-efficiency and load-balancing requirements of WSNs. Moreover, we present clustering and backbone formation operations as alternative applications of different VC infrastructures. For each VC sub-problem, we propose greedy heuristic based algorithms.Keywords: Wireless Sensor Networks, Link Monitoring, Graph Theory, Vertex Cover, NP-Hard Problem.KABLOSUZ SENSÖR AĞLARI İÇİN KÖŞE ÖRTME TABANLI BAĞLANTI İZLEME TEKNİKLERİÖzetKablosuz sensor ağlar (KSAlar) genellikle ortamdan algılayabilen ve bu verileri kablosuz iletişim yoluyla gönderebilen pille çalışan çok sayıda küçük düğümden oluşur. KSAlar askeri gözetim, sağlık hizmetleri, madenci güvenliği ve uzay keşfi gibi çok çeşitli uygulama alanlarına sahiptir. Kablosuz iletişimin doğasında var olan güvenlik zayıflıkları, KSAları gizli dinleme, sinyal bozma ve sahtekarlık gibi çeşitli saldırılara eğilimli hale getirebilmektedir. Bu durum, araştırmacıları bu saldırıların tespiti ve önlenmesine yönelik karşı önlemleri incelemeye yöneltmektedir. Çizge teorisi, iletişim ve güvenlik sorunları ile ilgili KSA sorunlarını çözmek için çok yararlı bir teorik temel sağlar. Önemli çizge teorik yapılardan biri köşe örtmedir (KÖ), bu yapıda her bir kenarın KÖ kümesindeki en az bir düğüme bitişik olacak şekilde çizgenin tüm kenarlarını kapsayacak bir dizi düğüm seçilmektedir. Verilen bir çizge için en az elemana sahip KÖ kümesini bulmak NP-zor bir problemdir. Bu makalede, KÖdeki düğümlerin güvenli noktalar olarak yapılandırıldığı bağlantı izlemeyi amaçlayan KÖ algoritmaları açıklanmaktadır. KSAların enerji verimliliği ve yük dengeleme gereksinimlerini karşılamak için, farklı çizge yapılarında KÖ problemlerinin ağırlık ve kapasite kısıtlı versiyonları gibi çeşitli türleri çalışılmaktadır. Ayrıca kümeleme ve omurga oluşturma işlemlerini farklı KÖ altyapılarının alternatif uygulamaları olarak sunulmaktadır. Her KÖ alt problemi için, açgözlü sezgisel tabanlı algoritmalar önerilmektedir.Anahtar Kelimeler: Kablosuz Sensör Ağları, Bağlantı İzleme, Çizge Teorisi, Kenar Örtme, NP-Zor Problem.

Structure of network, which is known as community detection in networks, has received a great attention in diverse topics, including social sciences, biological studies, politics, etc. There are a large number of studies and practical approaches that were designed to solve the problem of finding the structure of the network. The definition of complex network model based on clustering is a non-deterministic polynomial-time hardness (NP-hard) problem. There are no ideal techniques to define the clustering. Here, we present a statistical approach based on using the likelihood function of a Stochastic Block Model (SBM). The objective is to define the general model and select the best model with high quality. Therefore, integrating the Tabu Search method with Fuzzy c-Mean (FCM) is implemented in different settings. The experiments are designed to find the best structure for different types of networks by maximizing the objective functions. SBM selections are computed by applying two types of criteria, namely Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). The results show the ability of the proposed method to find the best community of the given networks.

AbstractThe study is a typological profile of 31 Indo-Aryan (IA) languages in the Hindukush-Karakoram-Western Himalayan region (covering NE Afghanistan, N Pakistan, and parts of Kashmir). Native speakers were recruited to provide comparative data. This data, supplemented by reputable descriptions or field notes, was evaluated against a number of WALS- or WALS-like features, enabling a fine-tuned characterization of each language, taking different linguistic domains into account (phonology, morphology, syntax, lexicon). The emerging patterns were compared with global distributions as well as with characteristic IA features and well-known areal patterns. Some features, mainly syntactic, turned out to be shared with IA in general, whereas others do have scattered reflexes in IA outside of the region but are especially prevalent in the region: large consonant inventories, tripartite pronominal case alignment, a high frequency of left-branching constructions, and multi-degree deictic systems. Yet other features display a high degree of diversity, often bundling subareally. Finally, there was a significant clustering of features that are not characterizing IA in general: tripartite affricate differentiation, retroflexion across several subsets, aspiration contrasts involving voiceless consonants only, tonal contrasts and 20-based numerals. This clustering forms a "hard core" at the centre of the region, gradually fading out toward its peripheries.

AbstractClassic and modern theories of rebel warfare emphasize the role of resource endowments. We demonstrate that intelligence gathering, made possible by these endowments, plays a critical role in determining specifics of how rebels launch complex attacks against better equipped government forces. We test implications of a theoretical model using highly detailed data about Afghan rebel attacks, insurgent‐led spy networks, and counterinsurgent operations. Leveraging quasi‐random variation in opium suitability, we find that improved rebel capacity is associated with (1) increased insurgent operations; (2) improved battlefield tactics through technological innovation, increased complexity, and attack clustering; and (3) increased effectiveness against security forces, especially harder targets. These results show that access to capital, coupled with intelligence gathering, meaningfully impacts how and where rebels fight.

Incorporating big data in urban planning has great potential for better modeling of urban dynamics and more efficiently allocating limited resources. However, big data may present new challenges for problem solutions. This research focuses on the p-median problem, one of the most widely used location models in urban and regional planning. Similar to many other location models, the p-median problem is non-deterministic polynomial-time hard (NP-hard), and solving large-sized p-median problems is difficult. This research proposes a high performance computing-based algorithm, random sampling and spatial voting, to solve large-sized p-median problems. Instead of solving a large p-median problem directly, a random sampling scheme is introduced to create smaller sub- p-median problems that can be solved in parallel efficiently. A spatial voting strategy is designed to evaluate the candidate facility sites for inclusion in obtaining the final problem solution. Tests with the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) data set show that random sampling and spatial voting provides high-quality solutions and reduces computing time significantly. Tests also demonstrate the dynamic scalability of the algorithm; it can start with a small amount of computing resources and scale up and down flexibly depending on the availability of the computing resources.

Part 5: Techniques and Analysis ; International audience ; Policy decisions in governmental models are often based on their perception and acceptance in the general public. Traditional methods for harvesting opinions like telephone or street surveys are time intensive and costly and direct interaction between a governmental member and the population is limited. Social media harbor the chance to easily get a high number of opinions and proposals in form of poll participation or interactive debate contributions.Especially debates about political topics can generate data which are hard to interpret because of its length and complexity. We propose a collection of methods to support a decision maker in gaining an overview over textual debates coming from several social media to save time and effort in manual analysis. Our approach enables an efficient decision making process by a combination of automatic topic clustering, sentiment analysis, filtering, and search functionalities aggregated in a graphical user interface. We present an implementation and a use case proving the usefulness of the proposed methodologies.

AbstractInternal working structures within parliaments are notoriously hard to capture. While analyses based on bill co-sponsorship work for the US Congress, this approach is not feasible in many parliamentary systems. Drawing on data from the European Parliament's legislative term of 2009–2014 this article shows that parliamentary questions can be another option. Members of the European Parliament may demand information from the Council or the Commission through oral questions. We take advantage of the fact that these questions are signed by their authors and construct a social network of members of the Parliament that support each other's oral questions. This allows investigating how members and their groups and committees cooperate to control both Council and Commission. Our approach helps to map out the internal structure of the party groups and explore which forces shape the global network. We find that cooperation is mostly driven by party group membership with ALDE, Green/EFA, and GUENGL turning out as the most cohesive groups while SD is internally rather loosely connected. The second strongest clustering characteristic is a legislators' native country.

Transition metal diborides are ceramic materials with potential applications as hard protective thin films and electrical contact materials. We investigate the possibility to obtain age hardening through isostructural clustering, including spinodal decomposition, or ordering-induced precipitation in ternary diboride alloys. By means of first-principles mixing thermodynamics calculations, 45 ternary (M1-xMxB2)-M-1-B-2 alloys comprising (MB2)-B-i (M-i = Mg, Al, Sc, Y, Ti, Zr, Hf, V, Nb, Ta) with AlB2 type structure are studied. In particular Al1-xTixB2 is found to be of interest for coherent isostructural decomposition with a strong driving force for phase separation, while having almost concentration independent a and c lattice parameters. The results are explained by revealing the nature of the electronic structure in these alloys, and in particular, the origin of the pseudogap at E-F in TiB2, ZrB2, and HfB2. ; Funding Agencies|Swedish Research Council (VR) [621-2011-4417, 621-2011-4249]; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University (Faculty Grant SFO-Mat-LiU) [2009-00971]; Knut and Alice Wallenberg Foundation; ERC [258509]