Guidelines for WB3 Part I: Desertification and land degradation: Identification of existing and potential prevention and mitigation strategies

These guidelines are a working instrument for conducting and moderating stakeholder workshops with a participatory approach to initiate a mutual learning process among local and external stakeholders. The overall aim of the workshop is to identify promising (existing and potential) strategies for land and water conservation for the selected study site. DESIRE (Desertification Mitigation and Remediation of Land) is a European Integrated Project. The DESIRE WB 3 methodology was developed by CDE and is based on experiences from Learning for Sustainability (LforS) and WOCAT.

Guidelines for WB3 Part II: Assessment of conservation strategies

These guidelines are a working instrument for the assessment and documentation of existing and potential strategies for land and water conservation (prevention and mitigation strategies) in DESIRE study sites. DESIRE (Desertification Mitigation and Remediation of Land) is a European Integrated Project. The DESIRE WB 3 methodology was developed by CDE and is based on experiences from Learning for Sustainability (LforS) and WOCAT.

CONMICOM – conflict mitigation in communities: LforS learning and simulation game

Three extended families live around a lake. One family are rice farmers, the second family are vegetable farmers, and the third are a family of livestock herders. All of them depend on the use of lake water for their production, and all of them need large quantities of water. All are dependent on the use of the lake water to secure their livelihood. In the game, the families are represented by their councils of elders. Each of the councils has to find means and ways to increase production in order to keep up with the growth of its family and their demands. This puts more and more pressure on the water resources, increasing the risk of overuse. Conflicts over water are about to emerge between the families. Each council of elders must try to pursue its families interests, while at the same time preventing excessive pressure on the water resources. Once a council of elders is no longer able to meet the needs of its family, it is excluded from the game. Will the parties cooperate or compete? To face the challenge of balancing economic well-being, sustainable resource management, and individual and collective interests, the three parties have a set of options for action at hand. These include power play to safeguard their own interests, communication and cooperation to negotiate with neighbours, and searching for alternatives to reduce pressure on existing water resources. During the game the players can experience how tensions may arise, increase and finally escalate. They realise what impact power play has and how alliances form, and the importance of trust-building measures, consensus and cooperation. From the insights gained, important conflict prevention and mitigation measures are derived in a debriefing session. The game is facilitated by a moderator, and lasts for 3-4 hours. Aim of the game: Each family pursues the objective of serving its own interests and securing its position through appropriate strategies and skilful negotiation, while at the same time optimising use of the water resources in a way that prevents their degradation. The end of the game is open. While the game may end by one or two families dropping out because they can no longer secure their subsistence, it is also possible that the three families succeed in creating a situation that allows them to meet their own needs as well as the requirements for sustainable water use in the long term. Learning objectives The game demonstrates how tension builds up, increases, and finally escalates; it shows how power positions work and alliances are formed; and it enables the players to experience the great significance of mutual agreement and cooperation. During the game and particularly during the debriefing and evaluation session it is important to link experiences made during the game to the players’ real-life experiences, and to discuss these links in the group. The resulting insights will provide a basis for deducing important conflict prevention and transformation measures.

Overcoming Liability of Newness through Socialization : How Legitimation Strategies of a New Venture evolve

Liability of newness, the tendency of new ventures to die early after market entry, results from lacking legitimacy in their new cultural context and according failure to acquire resources. Based on a longitudinal case study on repeated resource acquisition attempts of a new venture, we found that overcoming liability of newness depended on the socialization of the new venture to the normative environment on which it depended on for resources. Over time and across repeated resource acquisition attempts, socialization - the process of learning the use of legitimate symbols and their culturally contingent meanings - enabled the new venture to become the skillful cultural operator on which legitimation and resource acquisition was contingent. From our data, 'Accumulating a repertoire of legitimate symbols' and 'Assimilating the evaluations of resource-holders' emerged as the two primary mechanisms for new venture socialization. The study's contributions to related literature and its broader theoretical implications are discussed

Expanding Learning Time in Schools: Considering the Challenges of Implementation and the Potential Impact

Considering the broader context of school reform that is seeking education strategies that might deliver substantial impact, this article examines four questions related to the policy and practice of expanding learning time: (a) why do educators find the standard American school calendar insufficient to meet students’ educational needs, especially those of disadvantaged students? (b) how do educators implement a longer day and/or year, addressing concerns about both educational quality and costs? (c) what does research report about outcomes of expanding time in schools? and (d) what are the future prospects for increasing the number of expanded-time schools? The paper examines these questions by considering research, policy, and practice at the national level and, throughout, by drawing upon additional evidence from Massachusetts, one of the leading states in the expanded-time movement. In considering the latter two questions, the article explores the knowns and unknowns related to expanded learning time and offers suggestions for further research.

Society, family and learning. The role of home literacy environments

This paper explores the relation between society, family, and learning. In particular, it addresses the features of home literacy environments in low income families and their impact on children's pre-literacy skills and knowledge. Sixty-two four/five-year-old children and their mothers were randomly selected for this study. The mothers were interviewed using an adaptation of a family literacy environment survey (Whitehurst, 1992). The children were assessed with specific tests to examine the scope of their 'early literacy'. The results revealed significant variability in the features and practices of home literacy environments as well as in the children's emerging pre-literacy skills and knowledge. The correlation between the two variables shows low to moderate statistical significance. The implications of such findings are discussed. Additionally, the purpose of isolating relevant features of the children and their home environments is to identify specific indicators related to the literacy fostering process. Ultimately, the goal is to design adequate, timely, and systematic intervention strategies aimed at preventing difficulties related to written language learning in children that could be considered at risk.

Society, family and learning. The role of home literacy environments

This paper explores the relation between society, family, and learning. In particular, it addresses the features of home literacy environments in low income families and their impact on children's pre-literacy skills and knowledge. Sixty-two four/five-year-old children and their mothers were randomly selected for this study. The mothers were interviewed using an adaptation of a family literacy environment survey (Whitehurst, 1992). The children were assessed with specific tests to examine the scope of their 'early literacy'. The results revealed significant variability in the features and practices of home literacy environments as well as in the children's emerging pre-literacy skills and knowledge. The correlation between the two variables shows low to moderate statistical significance. The implications of such findings are discussed. Additionally, the purpose of isolating relevant features of the children and their home environments is to identify specific indicators related to the literacy fostering process. Ultimately, the goal is to design adequate, timely, and systematic intervention strategies aimed at preventing difficulties related to written language learning in children that could be considered at risk.

Society, family and learning. The role of home literacy environments

This paper explores the relation between society, family, and learning. In particular, it addresses the features of home literacy environments in low income families and their impact on children's pre-literacy skills and knowledge. Sixty-two four/five-year-old children and their mothers were randomly selected for this study. The mothers were interviewed using an adaptation of a family literacy environment survey (Whitehurst, 1992). The children were assessed with specific tests to examine the scope of their 'early literacy'. The results revealed significant variability in the features and practices of home literacy environments as well as in the children's emerging pre-literacy skills and knowledge. The correlation between the two variables shows low to moderate statistical significance. The implications of such findings are discussed. Additionally, the purpose of isolating relevant features of the children and their home environments is to identify specific indicators related to the literacy fostering process. Ultimately, the goal is to design adequate, timely, and systematic intervention strategies aimed at preventing difficulties related to written language learning in children that could be considered at risk.

Unlocking the Potential of Zambian Micro, Small and Medium Enterprises "Learning from the international best practices - the Southeast Asian Experience"

This paper examines the SMEs performance in Zambia and attempts to identify some practical lessons that Zambia can learn from Southeast Asian countries (with reference to Malaysia) in order to facilitate industrial development through unlocking the potential of its SMEs sector. Malaysia and Zambia were at the same level of economic development as evidenced by similar per capita incomes but Zambia has remained behind economically and its manufacturing sector has stagnated as if both countries did not have similar initial endowments. It therefore, becomes imperative that Zambia learns from such countries on how they managed to take-off economically with a focus on SME development. Training (education), research & development, market availability and technological advancement through establishment of industrial linkages coupled with cluster formation were some of the outstanding strategies identified that Zambia could use as a “key” to unlock its SMEs’ potential as it strives to meet the UN MDGs in particular halving its poverty levels by 2015 and also realizing its vision of becoming a middle income earner by 2030.

Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data

Mass spectrometry (MS) data provide a promising strategy for biomarker discovery. For this purpose, the detection of relevant peakbins in MS data is currently under intense research. Data from mass spectrometry are challenging to analyze because of their high dimensionality and the generally low number of samples available. To tackle this problem, the scientific community is becoming increasingly interested in applying feature subset selection techniques based on specialized machine learning algorithms. In this paper, we present a performance comparison of some metaheuristics: best first (BF), genetic algorithm (GA), scatter search (SS) and variable neighborhood search (VNS). Up to now, all the algorithms, except for GA, have been first applied to detect relevant peakbins in MS data. All these metaheuristic searches are embedded in two different filter and wrapper schemes coupled with Naive Bayes and SVM classifiers.

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.

Co-evolutionary and Reinforcement Learning Techniques Applied to Computer Go players

The objective of this thesis is model some processes from the nature as evolution and co-evolution, and proposing some techniques that can ensure that these learning process really happens and useful to solve some complex problems as Go game. The Go game is ancient and very complex game with simple rules which still is a challenge for the Artificial Intelligence. This dissertation cover some approaches that were applied to solve this problem, proposing solve this problem using competitive and cooperative co-evolutionary learning methods and other techniques proposed by the author. To study, implement and prove these methods were used some neural networks structures, a framework free available and coded many programs. The techniques proposed were coded by the author, performed many experiments to find the best configuration to ensure that co-evolution is progressing and discussed the results. Using co-evolutionary learning processes can be observed some pathologies which could impact co-evolution progress. In this dissertation is introduced some techniques to solve pathologies as loss of gradients, cycling dynamics and forgetting. According to some authors, one solution to solve these co-evolution pathologies is introduce more diversity in populations that are evolving. In this thesis is proposed some techniques to introduce more diversity and some diversity measurements for neural networks structures to monitor diversity during co-evolution. The genotype diversity evolved were analyzed in terms of its impact to global fitness of the strategies evolved and their generalization. Additionally, it was introduced a memory mechanism in the network neural structures to reinforce some strategies in the genes of the neurons evolved with the intention that some good strategies learned are not forgotten. In this dissertation is presented some works from other authors in which cooperative and competitive co-evolution has been applied. The Go board size used in this thesis was 9x9, but can be easily escalated to more bigger boards.The author believe that programs coded and techniques introduced in this dissertation can be used for other domains.

Active Learning and Dynamic Pricing Policies

In this paper, we address the problem of dynamic pricing to optimize the revenue coming from the sales of a limited inventory in a finite time-horizon. A priori, the demand is assumed to be unknown. The seller must learn on the fly. We first deal with the simplest case, involving only one class of product for sale. Furthermore the general situation is considered with a finite number of product classes for sale. In particular, a case in point is the sale of tickets for events related to culture and leisure; in this case, typically the tickets are sold months before the event, thus, uncertainty over actual demand levels is a very a common occurrence. We propose a heuristic strategy of adaptive dynamic pricing, based on experience gained from the past, taking into account, for each time period, the available inventory, the time remaining to reach the horizon, and the profit made in previous periods. In the computational simulations performed, the demand is updated dynamically based on the prices being offered, as well as on the remaining time and inventory. The simulations show a significant profit over the fixed-price strategy, confirming the practical usefulness of the proposed strategy. We develop a tool allowing us to test different dynamic pricing strategies designed to fit market conditions and seller s objectives, which will facilitate data analysis and decision-making in the face of the problem of dynamic pricing.

Learning Bayesian networks from data by the incremental compilation of new network polynomials

Probabilistic graphical models are a huge research field in artificial intelligence nowadays. The scope of this work is the study of directed graphical models for the representation of discrete distributions. Two of the main research topics related to this area focus on performing inference over graphical models and on learning graphical models from data. Traditionally, the inference process and the learning process have been treated separately, but given that the learned models structure marks the inference complexity, this kind of strategies will sometimes produce very inefficient models. With the purpose of learning thinner models, in this master thesis we propose a new model for the representation of network polynomials, which we call polynomial trees. Polynomial trees are a complementary representation for Bayesian networks that allows an efficient evaluation of the inference complexity and provides a framework for exact inference. We also propose a set of methods for the incremental compilation of polynomial trees and an algorithm for learning polynomial trees from data using a greedy score+search method that includes the inference complexity as a penalization in the scoring function.

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor’s infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.