Athens University of Economics and Business

Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar. Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). 2014.

The BioMagResBank (BMRB: www.bmrb.wisc.edu) is a repository for experimental and derived data gathered from nuclear magnetic resonance (NMR) spectroscopic studies of biological molecules. BMRB is a partner in the Worldwide Protein Data Bank (wwPDB). The BMRB archive consists of four main data depositories: (i) quantitative NMR spectral parameters for proteins, peptides, nucleic acids, carbohydrates and ligands or cofactors (assigned chemical shifts, coupling constants and peak lists) and derived data (relaxation parameters, residual dipolar couplings, hydrogen exchange rates, pK(a) values, etc.), (ii) databases for NMR restraints processed from original author depositions available from the Protein Data Bank, (iii) time-domain (raw) spectral data from NMR experiments used to assign spectral resonances and determine the structures of biological macromolecules and (iv) a database of one- and two-dimensional (1)H and (13)C one- and two-dimensional NMR spectra for over 250 metabolites. The BMRB website provides free access to all of these data. BMRB has tools for querying the archive and retrieving information and an ftp site (ftp.bmrb.wisc.edu) where data in the archive can be downloaded in bulk. Two BMRB mirror sites exist: one at the PDBj, Protein Research Institute, Osaka University, Osaka, Japan (bmrb.protein.osaka-u.ac.jp) and the other at CERM, University of Florence, Florence, Italy (bmrb.postgenomicnmr.net/). The site at Osaka also accepts and processes data depositions.

Distributed computer architectures labeled "peer-to-peer" are designed for the sharing of computer resources (content, storage, CPU cycles) by direct exchange, rather than requiring the intermediation or support of a centralized server or authority. Peer-to-peer architectures are characterized by their ability to adapt to failures and accommodate transient populations of nodes while maintaining acceptable connectivity and performance.Content distribution is an important peer-to-peer application on the Internet that has received considerable research attention. Content distribution applications typically allow personal computers to function in a coordinated manner as a distributed storage medium by contributing, searching, and obtaining digital content.In this survey, we propose a framework for analyzing peer-to-peer content distribution technologies. Our approach focuses on nonfunctional characteristics such as security, scalability, performance, fairness, and resource management potential, and examines the way in which these characteristics are reflected in---and affected by---the architectural design decisions adopted by current peer-to-peer systems.We study current peer-to-peer systems and infrastructure technologies in terms of their distributed object location and routing mechanisms, their approach to content replication, caching and migration, their support for encryption, access control, authentication and identity, anonymity, deniability, accountability and reputation, and their use of resource trading and management schemes.

The construct of trait emotional intelligence (trait EI or trait emotional self-efficacy) provides a comprehensive operationalization of emotion-related self-perceptions and dispositions. In the first part of the present study (N=274, 92 males), we performed two joint factor analyses to determine the location of trait EI in Eysenckian and Big Five factor space. The results showed that trait EI is a compound personality construct located at the lower levels of the two taxonomies. In the second part of the study, we performed six two-step hierarchical regressions to investigate the incremental validity of trait EI in predicting, over and above the Giant Three and Big Five personality dimensions, six distinct criteria (life satisfaction, rumination, two adaptive and two maladaptive coping styles). Trait EI incrementally predicted four criteria over the Giant Three and five criteria over the Big Five. The discussion addresses common questions about the operationalization of emotional intelligence as a personality trait.

The current Internet architecture was founded upon a host-centric communication model, which was appropriate for coping with the needs of the early Internet users. Internet usage has evolved however, with most users mainly interested in accessing (vast amounts of) information, irrespective of its physical location. This paradigm shift in the usage model of the Internet, along with the pressing needs for, among others, better security and mobility support, has led researchers into considering a radical change to the Internet architecture. In this direction, we have witnessed many research efforts investigating Information-Centric Networking (ICN) as a foundation upon which the Future Internet can be built. Our main aims in this survey are: (a) to identify the core functionalities of ICN architectures, (b) to describe the key ICN proposals in a tutorial manner, highlighting the similarities and differences among them with respect to those core functionalities, and (c) to identify the key weaknesses of ICN proposals and to outline the main unresolved research challenges in this area of networking research.

Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammad AL-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orphée De Clercq, Véronique Hoste, Marianna Apidianaki, Xavier Tannier, Natalia Loukachevitch, Evgeniy Kotelnikov, Nuria Bel, Salud María Jiménez-Zafra, Gülşen Eryiğit. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). 2016.

SemEval-2015 Task 12, a continuation of SemEval-2014 Task 4, aimed to foster research beyond sentence-or text-level sentiment classification towards Aspect Based Sentiment Analysis. The goal is to identify opinions expressed about specific entities (e.g., laptops) and their aspects (e.g., price). The task provided manually annotated reviews in three domains (restaurants, laptops and hotels), and a common evaluation procedure. It attracted 93 submissions from 16 teams.

Abstract In this study we revisit some fundamental questions that are increasingly at the heart of current strategic management discourse regarding the relative impact of industry and firm‐specific factors on sustainable competitive advantage. We explore this issue by referring to respective assertions of two major perspectives that dominate the literature over the last two decades: the Porter framework of competitive strategy and the more recent resource‐based view of the firm. A composite model is proposed which elaborates upon both perspectives' divergent causal logic with respect to the conditions relevant for firm success. Empirical findings suggest that industry and firm specific effects are both important but explain different dimensions of performance. Where industry forces influence market performance and profitability, firm assets act upon accomplishments in the market arena (i.e., market performance), and via the latter, to profitability. The paper concludes with directions for future research that will seek to integrate both content and process aspects of firm behavior. Copyright © 2001 John Wiley & Sons, Ltd.

This study reviews quantitative empirical studies of change recipients’ reactions to organizational change. The authors reviewed studies published between 1948 and 2007, out of which 79 met the criteria of being quantitative studies of change recipients’ reactions to an organizational change. Through an inductive review, the authors unravel a model of (a) explicit reactions to change, in which these reactions are conceptualized as tridimensional attitudes; (b) reaction antecedents that comprise prechange antecedents (viz., change recipient characteristics and internal context) and change antecedents (viz., change process, perceived benefit/harm, and change content); and (c) change consequences, including work-related and personal consequences. On the basis of their review the authors conclude by proposing directions for future research and practical managerial implications.

Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user's navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented.

Abstract Systems linking people and nature, known as social-ecological systems, are increasingly understood as complex adaptive systems. Essential features of these complex adaptive systems – such as nonlinear feedbacks, strategic interactions, individual and spatial heterogeneity, and varying time scales – pose substantial challenges for modeling. However, ignoring these characteristics can distort our picture of how these systems work, causing policies to be less effective or even counterproductive. In this paper we present recent developments in modeling social-ecological systems, illustrate some of these challenges with examples related to coral reefs and grasslands, and identify the implications for economic and policy analysis.

Abstract With big data analytics growing rapidly in popularity, academics and practitioners have been considering the means through which they can incorporate the shifts these technologies bring into their competitive strategies. Drawing on the resource‐based view, the dynamic capabilities view, and on recent literature on big data analytics, this study examines the indirect relationship between a big data analytics capability (BDAC) and two types of innovation capabilities: incremental and radical. The study extends existing research by proposing that BDACs enable firms to generate insight that can help strengthen their dynamic capabilities, which in turn positively impact incremental and radical innovation capabilities. To test their proposed research model, the authors used survey data from 175 chief information officers and IT managers working in Greek firms. By means of partial least squares structural equation modelling, the results confirm the authors’ assumptions regarding the indirect effect that BDACs have on innovation capabilities. Specifically, they find that dynamic capabilities fully mediate the effect on both incremental and radical innovation capabilities. In addition, under conditions of high environmental heterogeneity, the impact of BDACs on dynamic capabilities and, in sequence, incremental innovation capability is enhanced, while under conditions of high environmental dynamism the effect of dynamic capabilities on incremental innovation capabilities is amplified.

Spatio-temporal databases deal with geometries changing over time. The goal of our work is to provide a DBMS data model and query language capable of handling such time-dependent geometries, including those changing continuously that describe moving objects . Two fundamental abstractions are moving point and moving region , describing objects for which only the time-dependent position, or position and extent, respectively, are of interest. We propose to present such time-dependent geometries as attribute data types with suitable operations, that is, to provide an abstract data type extension to a DBMS data model and query language. This paper presents a design of such a system of abstract data types. It turns out that besides the main types of interest, moving point and moving region, a relatively large number of auxiliary data types are needed. For example, one needs a line type to represent the projection of a moving point into the plane, or a “moving real” to represent the time-dependent distance of two points. It then becomes crucial to achieve (i) orthogonality in the design of the system, i.e., type constructors can be applied unifomly; (ii) genericity and consistency of operations, i.e., operations range over as many types as possible and behave consistently; and (iii) closure and consistency between structure and operations of nontemporal and related temporal types. Satisfying these goal leads to a simple and expressive system of abstract data types that may be integrated into a query language to yield a powerful language for querying spatio-temporal data, including moving objects. The paper formally defines the types and operations, offers detailed insight into the considerations that went into the design, and exemplifies the use of the abstract data types using SQL. The paper offers a precise and conceptually clean foundation for implementing a spatio-temporal DBMS extension.

Aquaculture is the fastest growing food sector and continues to expand alongside terrestrial crop and livestock production. Using portfolio theory as a conceptual framework, we explore how current interconnections between the aquaculture, crop, livestock, and fisheries sectors act as an impediment to, or an opportunity for, enhanced resilience in the global food system given increased resource scarcity and climate change. Aquaculture can potentially enhance resilience through improved resource use efficiencies and increased diversification of farmed species, locales of production, and feeding strategies. However, aquaculture's reliance on terrestrial crops and wild fish for feeds, its dependence on freshwater and land for culture sites, and its broad array of environmental impacts diminishes its ability to add resilience. Feeds for livestock and farmed fish that are fed rely largely on the same crops, although the fraction destined for aquaculture is presently small (∼4%). As demand for high-value fed aquaculture products grows, competition for these crops will also rise, as will the demand for wild fish as feed inputs. Many of these crops and forage fish are also consumed directly by humans and provide essential nutrition for low-income households. Their rising use in aquafeeds has the potential to increase price levels and volatility, worsening food insecurity among the most vulnerable populations. Although the diversification of global food production systems that includes aquaculture offers promise for enhanced resilience, such promise will not be realized if government policies fail to provide adequate incentives for resource efficiency, equity, and environmental protection.

This paper investigates the relationship between the process of strategic decision-making and management and contextual factors. First, drawing on a sample of strategic decisions, it analyzes the process through which they are taken, into seven dimensions: comprehensiveness/rationality, financial reporting, rule formalization, hierarchical decentralization, lateral communication, politicization, problem-solving dissension. Second, these process dimensions are related to (1) decision-specific characteristics, both perceived characteristics and objective typologies of strategic decisions, (2) top management characteristics, and (3) contextual factors referring to external corporate environment and internal firm characteristics. Overall, the results support the view that strategic decision processes are shaped by a multiplicity of factors, in all these categories. But the most striking finding is that decision-specific characteristics appear to have the most important influence on the strategic decision-making process, as decisions with different decision-specific characteristics are handled through different processes. The evident dominance of decision-specific characteristics over management and contextual factors enriches the traditional ‘external control’ vs. ‘strategic choice’ debate in the area of strategic management. An interpretation of results is attempted and policy implications are derived. © 1998 John Wiley & Sons, Ltd.

The results of the present longitudinal study demonstrate the importance of implicit leadership theories (ILTs) for the quality of leader-member exchanges (LMX) and employees' organizational commitment, job satisfaction, and well-being. Results based on a sample of 439 employees who completed the study questionnaires at 2 time points showed that the closer employees perceived their actual manager's profile to be to the ILTs they endorsed, the better the quality of LMX. Results also indicated that the implicit-explicit leadership traits difference had indirect effects on employee attitudes and well-being. These findings were consistent across employee groups that differed in terms of job demand and the duration of manager-employee relation, but not in terms of motivation. Furthermore, crossed-lagged modeling analyses of the longitudinal data explored the possibility of reciprocal effects between implicit-explicit leadership traits difference and LMX and provided support for the initially hypothesized direction of causal effects.

Big data analytics has been widely regarded as a breakthrough technological development in academic and business communities. Despite the growing number of firms that are launching big data initiatives, there is still limited understanding on how firms translate the potential of such technologies into business value. The literature argues that to leverage big data analytics and realize performance gains, firms must develop strong big data analytics capabilities. Nevertheless, most studies operate under the assumption that there is limited heterogeneity in the way firms build their big data analytics capabilities and that related resources are of similar importance regardless of context. This paper draws on complexity theory and investigates the configurations of resources and contextual factors that lead to performance gains from big data analytics investments. Our empirical investigation followed a mixed methods approach using survey data from 175 chief information officers and IT managers working in Greek firms, and three case studies to show that depending on the context, big data analytics resources differ in significance when considering performance gains. Applying a fuzzy-set qualitative comparative analysis (fsQCA) method on the quantitative data, we show that there are four different patterns of elements surrounding big data analytics that lead to high performance. Outcomes of the three case studies highlight the inter-relationships between these elements and outline challenges that organizations face when orchestrating big data analytics resources.

Networks (or graphs) appear as dominant structures in diverse domains, including sociology, biology, neu-roscience and computer science. In most of the aforementioned cases graphs are directed – in the sense that there is directionality on the edges, making the semantics of the edges non symmetric as the source node transmits some property to the target one but not vice versa. An interesting feature that real networks present is the clustering or community structure property, under which the graph topology is organized into modules commonly called communities or clusters. The essence here is that nodes of the same community are highly similar while on the contrary, nodes across communities present low similarity. Revealing the un-derlying community structure of directed complex networks has become a crucial and interdisciplinary topic with a plethora of relevant application domains. Therefore, naturally there is a recent wealth of research production in the area of mining directed graphs – with clustering being the primary method sought and the primary tool for community detection and evaluation. The goal of this paper is to offer an in-depth comparative review of the methods presented so far for clustering directed networks along with the relevant necessary methodological background and also related applications. The survey commences by offering a concise review of the fundamental concepts and methodological base on which graph clustering algorithms

Physical activity has a fundamental role in the prevention and treatment of chronic disease. The precise measurement of physical activity is key to many surveillance and epidemiological studies investigating trends and associations with disease. Public health initiatives aimed at increasing physical activity rely on the measurement of physical activity to monitor their effectiveness. Physical activity is multidimensional, and a complex behaviour to measure; its various domains are often misunderstood. Inappropriate or crude measures of physical activity have serious implications, and are likely to lead to misleading results and underestimate effect size. In this review, key definitions and theoretical aspects, which underpin the measurement of physical activity, are briefly discussed. Methodologies particularly suited for use in epidemiological research are reviewed, with particular reference to their validity, primary outcome measure and considerations when using each in the field. It is acknowledged that the choice of method may be a compromise between accuracy level and feasibility, but the ultimate choice of tool must suit the stated aim of the research. A framework is presented to guide researchers on the selection of the most suitable tool for use in a specific study.

BACKGROUND: This article provides an overview of the first BIOASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BIOASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies. RESULTS: The 2013 BIOASQ competition comprised two tasks, Task 1a and Task 1b. In Task 1a participants were asked to automatically annotate new PUBMED documents with MESH headings. Twelve teams participated in Task 1a, with a total of 46 system runs submitted, and one of the teams performing consistently better than the MTI indexer used by NLM to suggest MESH headings to curators. Task 1b used benchmark datasets containing 29 development and 282 test English questions, along with gold standard (reference) answers, prepared by a team of biomedical experts from around Europe and participants had to automatically produce answers. Three teams participated in Task 1b, with 11 system runs. The BIOASQ infrastructure, including benchmark datasets, evaluation mechanisms, and the results of the participants and baseline methods, is publicly available. CONCLUSIONS: A publicly available evaluation infrastructure for biomedical semantic indexing and QA has been developed, which includes benchmark datasets, and can be used to evaluate systems that: assign MESH headings to published articles or to English questions; retrieve relevant RDF triples from ontologies, relevant articles and snippets from PUBMED Central; produce "exact" and paragraph-sized "ideal" answers (summaries). The results of the systems that participated in the 2013 BIOASQ competition are promising. In Task 1a one of the systems performed consistently better from the NLM's MTI indexer. In Task 1b the systems received high scores in the manual evaluation of the "ideal" answers; hence, they produced high quality summaries as answers. Overall, BIOASQ helped obtain a unified view of how techniques from text classification, semantic indexing, document and passage retrieval, question answering, and text summarization can be combined to allow biomedical experts to obtain concise, user-understandable answers to questions reflecting their real information needs.