Transforming aquatic agricultural systems towards gender equality: a five country review

Aquatic agricultural systems (AAS) are systems in which the annual production dynamics of freshwater and/or coastal ecosystems contribute significantly to total household income. Improving the livelihood security and wellbeing of the estimated 250 million poor people dependent on AAS in Bangladesh, Cambodia, the Philippines, the Solomon Islands and Zambia is the goal of the Worldfish Center-led Consortium Research Program (CRP), “Harnessing the development potential of aquatic agricultural systems for development.” One component expected to contribute to sustainably achieving this goal is enhancing the gender and wider social equity of the social, economic and political systems within which the AAS function. The CRP’s focus on social equity, and particularly gender equity, responds to the limited progress to date in enhancing the inclusiveness of development outcomes through interventions that offer improved availability of resources and technologies without addressing the wider social constraints that marginalized populations face in making use of them. The CRP aims to both offer improved availability and address the wider social constraints in order to determine whether a multi-level approach that engages with individuals, households and communities, as well as the wider social, economic and political contexts in which they function, is more successful in extending development’s benefits to women and other excluded groups. Designing the research in development initiatives to test this hypothesis requires a solid understanding of each CRP country’s social, cultural and economic contexts and of the variations across them. This paper provides an initial input into developing this knowledge, based on a review of literature on agriculture, aquaculture and gender relations within the five focal countries. Before delving into the findings of the literature review, the paper first justifies the expectation that successfully achieving lasting wellbeing improvements for poor women and men dependent on AAS rests in part on advances in gender equity, and in light of this justification, presents the AAS CRP’s conceptual framew

POMDP-based statistical spoken dialog systems: A review

Statistical dialog systems (SDSs) are motivated by the need for a data-driven framework that reduces the cost of laboriously handcrafting complex dialog managers and that provides robustness against the errors created by speech recognizers operating in noisy environments. By including an explicit Bayesian model of uncertainty and by optimizing the policy via a reward-driven process, partially observable Markov decision processes (POMDPs) provide such a framework. However, exact model representation and optimization is computationally intractable. Hence, the practical application of POMDP-based systems requires efficient algorithms and carefully constructed approximations. This review article provides an overview of the current state of the art in the development of POMDP-based spoken dialog systems. © 1963-2012 IEEE.

Library management systems in the UK: 1960s-1980s

Tedd, L.A. (2007). Library management systems in the UK: 1960s-1980s. Library History, 23(4),301-316 Originally published (as above) by Maney Publishing.

Granularity and disaggregation in compositional modelling with applications to ecological systems

J. Keppens and Q. Shen. Granularity and disaggregation in compositional modelling with applications to ecological systems. Applied Intelligence, 25(3):269-292, 2006.

An investigation of innovation and knowledge creation in virtual worlds

The Internet and World Wide Web have had, and continue to have, an incredible impact on our civilization. These technologies have radically influenced the way that society is organised and the manner in which people around the world communicate and interact. The structure and function of individual, social, organisational, economic and political life begin to resemble the digital network architectures upon which they are increasingly reliant. It is increasingly difficult to imagine how our ‘offline’ world would look or function without the ‘online’ world; it is becoming less meaningful to distinguish between the ‘actual’ and the ‘virtual’. Thus, the major architectural project of the twenty-first century is to “imagine, build, and enhance an interactive and ever changing cyberspace” (Lévy, 1997, p. 10). Virtual worlds are at the forefront of this evolving digital landscape. Virtual worlds have “critical implications for business, education, social sciences, and our society at large” (Messinger et al., 2009, p. 204). This study focuses on the possibilities of virtual worlds in terms of communication, collaboration, innovation and creativity. The concept of knowledge creation is at the core of this research. The study shows that scholars increasingly recognise that knowledge creation, as a socially enacted process, goes to the very heart of innovation. However, efforts to build upon these insights have struggled to escape the influence of the information processing paradigm of old and have failed to move beyond the persistent but problematic conceptualisation of knowledge creation in terms of tacit and explicit knowledge. Based on these insights, the study leverages extant research to develop the conceptual apparatus necessary to carry out an investigation of innovation and knowledge creation in virtual worlds. The study derives and articulates a set of definitions (of virtual worlds, innovation, knowledge and knowledge creation) to guide research. The study also leverages a number of extant theories in order to develop a preliminary framework to model knowledge creation in virtual worlds. Using a combination of participant observation and six case studies of innovative educational projects in Second Life, the study yields a range of insights into the process of knowledge creation in virtual worlds and into the factors that affect it. The study’s contributions to theory are expressed as a series of propositions and findings and are represented as a revised and empirically grounded theoretical framework of knowledge creation in virtual worlds. These findings highlight the importance of prior related knowledge and intrinsic motivation in terms of shaping and stimulating knowledge creation in virtual worlds. At the same time, they highlight the importance of meta-knowledge (knowledge about knowledge) in terms of guiding the knowledge creation process whilst revealing the diversity of behavioural approaches actually used to create knowledge in virtual worlds and. This theoretical framework is itself one of the chief contributions of the study and the analysis explores how it can be used to guide further research in virtual worlds and on knowledge creation. The study’s contributions to practice are presented as actionable guide to simulate knowledge creation in virtual worlds. This guide utilises a theoretically based classification of four knowledge-creator archetypes (the sage, the lore master, the artisan, and the apprentice) and derives an actionable set of behavioural prescriptions for each archetype. The study concludes with a discussion of the study’s implications in terms of future research.

A framework for state-based time-series analysis and prediction

Time-series analysis and prediction play an important role in state-based systems that involve dealing with varying situations in terms of states of the world evolving with time. Generally speaking, the world in the discourse persists in a given state until something occurs to it into another state. This paper introduces a framework for prediction and analysis based on time-series of states. It takes a time theory that addresses both points and intervals as primitive time elements as the temporal basis. A state of the world under consideration is defined as a set of time-varying propositions with Boolean truth-values that are dependent on time, including properties, facts, actions, events and processes, etc. A time-series of states is then formalized as a list of states that are temporally ordered one after another. The framework supports explicit expression of both absolute and relative temporal knowledge. A formal schema for expressing general time-series of states to be incomplete in various ways, while the concept of complete time-series of states is also formally defined. As applications of the formalism in time-series analysis and prediction, we present two illustrating examples.

A Competence-Based Course Authoring Concept for Learning Platforms with Legacy Assignment Tools

This paper is concerned with several of the most important aspects of Competence-Based Learning (CBL): course authoring, assignments, and categorization of learning content. The latter is part of the so-called Bologna Process (BP) and can effectively be supported by integrating knowledge resources like, e.g., standardized skill and competence taxonomies into the target implementation approach, aiming at making effective use of an open integration architecture while fostering the interoperability of hybrid knowledge-based e-learning solutions. Modern scenarios ask for interoperable software solutions to seamlessly integrate existing e-learning infrastructures and legacy tools with innovative technologies while being cognitively efficient to handle. In this way, prospective users are enabled to use them without learning overheads. At the same time, methods of Learning Design (LD) in combination with CBL are getting more and more important for production and maintenance of easy to facilitate solutions. We present our approach of developing a competence-based course-authoring and assignment support software. It is bridging the gaps between contemporary Learning Management Systems (LMS) and established legacy learning infrastructures by embedding existing resources via Learning Tools Interoperability (LTI). Furthermore, the underlying conceptual architecture for this integration approach will be explained. In addition, a competence management structure based on knowledge technologies supporting standardized skill and competence taxonomies will be introduced. The overall goal is to develop a software solution which will not only flawlessly merge into a legacy platform and several other learning environments, but also remain intuitively usable. As a proof of concept, the so-called platform independent conceptual architecture model will be validated by a concrete use case scenario.

Pre-charging and DC Fault Ride-Through of Hybri MMC Based HVDC Systems

Compared to half-bridge based MMCs, full-bridge based systems have the advantage of blocking dc fault, but at the expense of increased power semiconductors and power losses. In view of the relationships among ac/dc voltages and currents in full-bridge based MMC with the negative voltage state, this paper provides a detailed analysis on the link between capacitor voltage variation and the maximum modulation index. A hybrid MMC, consisting of mixed half-bridge and full-bridge circuits to combine their respective advantages is investigated in terms of its pre-charging process and transient dc fault ride-through capability. Simulation and experiment results demonstrate the feasibility and validity of the proposed strategy for a full-bridge based MMC and the hybrid MMC.

A Characteristic Function Approach to Inconsistency Measures for Knowledge Bases.

Knowledge is an important component in many intelligent systems.
Since items of knowledge in a knowledge base can be conflicting, especially if
there are multiple sources contributing to the knowledge in this base, significant
research efforts have been made on developing inconsistency measures for
knowledge bases and on developing merging approaches. Most of these efforts
start with flat knowledge bases. However, in many real-world applications, items
of knowledge are not perceived with equal importance, rather, weights (which
can be used to indicate the importance or priority) are associated with items of
knowledge. Therefore, measuring the inconsistency of a knowledge base with
weighted formulae as well as their merging is an important but difficult task. In
this paper, we derive a numerical characteristic function from each knowledge
base with weighted formulae, based on the Dempster-Shafer theory of evidence.
Using these functions, we are able to measure the inconsistency of the knowledge
base in a convenient and rational way, and are able to merge multiple knowledge
bases with weighted formulae, even if knowledge in these bases may be
inconsistent. Furthermore, by examining whether multiple knowledge bases are
dependent or independent, they can be combined in different ways using their
characteristic functions, which cannot be handled (or at least have never been
considered) in classic knowledge based merging approaches in the literature.

TINSEL – a web-based intelligent tutoring system on electrical installation design

This paper presents a project consisting on the development of an Intelligent Tutoring System, for training and support concerning the development of electrical installation projects to be used by electrical engineers, technicians and students. One of the major goals of this project is to devise a teaching model based on Intelligent Tutoring techniques, considering not only academic knowledge but also other types of more empirical knowledge, able to achieve successfully the training of electrical installation design.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

Barriers of evidence based policy making in iran's health system

La formation des sociétés fondées sur la connaissance, le progrès de la technologie de communications et un meilleur échange d'informations au niveau mondial permet une meilleure utilisation des connaissances produites lors des décisions prises dans le système de santé. Dans des pays en voie de développement, quelques études sont menées sur des obstacles qui empêchent la prise des décisions fondées sur des preuves (PDFDP) alors que des études similaires dans le monde développé sont vraiment rares. L'Iran est le pays qui a connu la plus forte croissance dans les publications scientifiques au cours de ces dernières années, mais la question qui se pose est la suivante : quels sont les obstacles qui empêchent l'utilisation de ces connaissances de même que celle des données mondiales? Cette étude embrasse trois articles consécutifs. Le but du premier article a été de trouver un modèle pour évaluer l'état de l'utilisation des connaissances dans ces circonstances en Iran à l’aide d'un examen vaste et systématique des sources suivie par une étude qualitative basée sur la méthode de la Grounded Theory. Ensuite au cours du deuxième et troisième article, les obstacles aux décisions fondées sur des preuves en Iran, sont étudiés en interrogeant les directeurs, les décideurs du secteur de la santé et les chercheurs qui travaillent à produire des preuves scientifiques pour la PDFDP en Iran. Après avoir examiné les modèles disponibles existants et la réalisation d'une étude qualitative, le premier article est sorti sous le titre de «Conception d'un modèle d'application des connaissances». Ce premier article sert de cadre pour les deux autres articles qui évaluent les obstacles à «pull» et «push» pour des PDFDP dans le pays. En Iran, en tant que pays en développement, les problèmes se situent dans toutes les étapes du processus de production, de partage et d’utilisation de la preuve dans la prise de décision du système de santé. Les obstacles qui existent à la prise de décision fondée sur des preuves sont divers et cela aux différents niveaux; les solutions multi-dimensionnelles sont nécessaires pour renforcer l'impact de preuves scientifiques sur les prises de décision. Ces solutions devraient entraîner des changements dans la culture et le milieu de la prise de décision afin de valoriser la prise de décisions fondées sur des preuves. Les critères de sélection des gestionnaires et leur nomination inappropriée ainsi que leurs remplaçants rapides et les différences de paiement dans les secteurs public et privé peuvent affaiblir la PDFDP de deux façons : d’une part en influant sur la motivation des décideurs et d'autre part en détruisant la continuité du programme. De même, tandis que la sélection et le remplacement des chercheurs n'est pas comme ceux des gestionnaires, il n'y a aucun critère pour encourager ces deux groupes à soutenir le processus décisionnel fondés sur des preuves dans le secteur de la santé et les changements ultérieurs. La sélection et la promotion des décideurs politiques devraient être basées sur leur performance en matière de la PDFDP et les efforts des universitaires doivent être comptés lors de leurs promotions personnelles et celles du rang de leur institution. Les attitudes et les capacités des décideurs et des chercheurs devraient être encouragés en leur donnant assez de pouvoir et d’habiliter dans les différentes étapes du cycle de décision. Cette étude a révélé que les gestionnaires n'ont pas suffisamment accès à la fois aux preuves nationales et internationales. Réduire l’écart qui sépare les chercheurs des décideurs est une étape cruciale qui doit être réalisée en favorisant la communication réciproque. Cette question est très importante étant donné que l'utilisation des connaissances ne peut être renforcée que par l'étroite collaboration entre les décideurs politiques et le secteur de la recherche. Dans ce but des programmes à long terme doivent être conçus ; la création des réseaux de chercheurs et de décideurs pour le choix du sujet de recherche, le classement des priorités, et le fait de renforcer la confiance réciproque entre les chercheurs et les décideurs politiques semblent être efficace.

Capturing Emergent Semantics from Social Annotation Systems

The ongoing growth of the World Wide Web, catalyzed by the increasing possibility of ubiquitous access via a variety of devices, continues to strengthen its role as our prevalent information and commmunication medium. However, although tools like search engines facilitate retrieval, the task of finally making sense of Web content is still often left to human interpretation. The vision of supporting both humans and machines in such knowledge-based activities led to the development of different systems which allow to structure Web resources by metadata annotations. Interestingly, two major approaches which gained a considerable amount of attention are addressing the problem from nearly opposite directions: On the one hand, the idea of the Semantic Web suggests to formalize the knowledge within a particular domain by means of the "top-down" approach of defining ontologies. On the other hand, Social Annotation Systems as part of the so-called Web 2.0 movement implement a "bottom-up" style of categorization using arbitrary keywords. Experience as well as research in the characteristics of both systems has shown that their strengths and weaknesses seem to be inverse: While Social Annotation suffers from problems like, e. g., ambiguity or lack or precision, ontologies were especially designed to eliminate those. On the contrary, the latter suffer from a knowledge acquisition bottleneck, which is successfully overcome by the large user populations of Social Annotation Systems. Instead of being regarded as competing paradigms, the obvious potential synergies from a combination of both motivated approaches to "bridge the gap" between them. These were fostered by the evidence of emergent semantics, i. e., the self-organized evolution of implicit conceptual structures, within Social Annotation data. While several techniques to exploit the emergent patterns were proposed, a systematic analysis - especially regarding paradigms from the field of ontology learning - is still largely missing. This also includes a deeper understanding of the circumstances which affect the evolution processes. This work aims to address this gap by providing an in-depth study of methods and influencing factors to capture emergent semantics from Social Annotation Systems. We focus hereby on the acquisition of lexical semantics from the underlying networks of keywords, users and resources. Structured along different ontology learning tasks, we use a methodology of semantic grounding to characterize and evaluate the semantic relations captured by different methods. In all cases, our studies are based on datasets from several Social Annotation Systems. Specifically, we first analyze semantic relatedness among keywords, and identify measures which detect different notions of relatedness. These constitute the input of concept learning algorithms, which focus then on the discovery of synonymous and ambiguous keywords. Hereby, we assess the usefulness of various clustering techniques. As a prerequisite to induce hierarchical relationships, our next step is to study measures which quantify the level of generality of a particular keyword. We find that comparatively simple measures can approximate the generality information encoded in reference taxonomies. These insights are used to inform the final task, namely the creation of concept hierarchies. For this purpose, generality-based algorithms exhibit advantages compared to clustering approaches. In order to complement the identification of suitable methods to capture semantic structures, we analyze as a next step several factors which influence their emergence. Empirical evidence is provided that the amount of available data plays a crucial role for determining keyword meanings. From a different perspective, we examine pragmatic aspects by considering different annotation patterns among users. Based on a broad distinction between "categorizers" and "describers", we find that the latter produce more accurate results. This suggests a causal link between pragmatic and semantic aspects of keyword annotation. As a special kind of usage pattern, we then have a look at system abuse and spam. While observing a mixed picture, we suggest that an individual decision should be taken instead of disregarding spammers as a matter of principle. Finally, we discuss a set of applications which operationalize the results of our studies for enhancing both Social Annotation and semantic systems. These comprise on the one hand tools which foster the emergence of semantics, and on the one hand applications which exploit the socially induced relations to improve, e. g., searching, browsing, or user profiling facilities. In summary, the contributions of this work highlight viable methods and crucial aspects for designing enhanced knowledge-based services of a Social Semantic Web.