Miracle-dz/UniFAR-FTUs huggingface dataset:
We grow in the shade of each other: A study of connectedness, empowerment and learning in the middle years of schooling

Learning is enabled in an environment that promotes connectedness. This belief led me to an exploration of connectedness and the discovery that connectedness has more than one connotation in the literature. For some it means making connections within curriculum areas, which is closely associated with the understanding that connectedness means teaching and learning within a community of learners. Another body of literature understands connectedness as a person’s sense of belonging within the family, school and wider community. Embedded in all these understandings of the term is either implicit or explicit reference to empowerment. An exploration of learning necessarily involves an exploration of students and teachers perceptions of effective learning. The exploration of learning focuses on: • teacher and students understanding of learning • student expectations and achievements within the classroom • the opportunities for participation and contribution. The various understandings of connectedness, empowerment and learning are linked in the exploration of the following themes within the classroom: 1. Building caring relationships 2. Setting high and achievable expectations and 3. Providing opportunities for participation and contribution (Bernard, 1991; 1997; MindMatters, 2000). The context is the middle years of schooling as the last 10 years has produced research that delivers findings asking teachers in the middle years to negotiate a curriculum that is based on people. Effective teaching and learning is essential if students are to achieve their potential, should be cooperative and be fostered within a reflective community atmosphere. Relationships are to the fore in all concepts of effective middle schooling and this case study explores relationship as they exist at Garden College in year seven. It is these relationships that promote a sense of belonging to and empowerment within the learning community, thus enabling learning. If schools are to “expedite the development of effective middle schooling” (Schools Council, National Board of Employment, Education and Training, 1993, p. 65), by addressing the issues highlighted above, I believe the concepts of connectedness, empowerment and learning must be fully explored by the community of learners in each school.
Authenticated Group Key Agreement Protocol for Unbalanced Wireless Mobile Networks

In 2004, Bresson et al. proposed an authentication and group key agreement protocol for unbalanced wireless networks. Tseng recently proposed a novel secure protocol to improve Bresson et al.’s protocol. However, both protocols are insecure against the so-called impersonation attacks. This paper proposes a mutual authenticated group key agreement protocol based on RSA, discrete logarithms (DL) and one-way hash function (OHF) assumptions. The proposed protocol is more secure and efficient than previously proposed protocols for unbalanced wireless mobile networks.
Modelling the motivational state of the learner in a Vygotskyan inspired ITS

An analysis of the influence of motivation on the ZPD is being carried out. To investigate the impact of motivational factors on the ZPD the inclusion of a motivation differentiator layer into the Ecolab [11] is proposed. The mechanisms and values to measure the motivational states of the learners and scaffold the motivational strategies to adjust them to individual pupils are outlined. The main research question on which this proposal is centred is: How can motivational factors be incorporated within a Vygotskyan framework for an ITS?
International Environmental Modelling and Software Society (iemss) 2010 International Congress on Environmental Modelling and Software Modelling for Environment's Sake, Fifth Biennial Meeting, Ottawa, Canada Agent Swarm Optimization: a Paradigm to Tackle Complex Problems. Application to Water Distri

Agent Swarm Optimization (ASO) is a generalization of Particle Swarm Optimization (PSO) orientated towards distributed artificial intelligence, taking as a base the concept of multi-agent systems. It is aimed at supporting decision-making processes by solving either single or multi-objective optimization problems. ASO offers a common framework for the plurality of co-existent population-based algorithms and other heuristics. A particle from a PSO swarm, an ant from an ACO (Ant Colony Optimization) system, and a chromosome from a GA (Genetic Algorithm) structure do exhibit different behaviour. Yet, they all share a common feature: each represents a potential solution for the problem to be solved. In a combined environment, a PSO particle could help reinforce pheromone on the ants' paths; an ant could be reproduced with a chromosome; a chromosome could be the leader of a particle swarm, and so on. This framework is a dynamic environment where new agents/swarms can be added in real time to contribute to the solution of the problem. During the solution process, the own user can add new agents/swarms to the environment and even contribute to the solution process with problem-based personal proposals. In this work the ASO framework is described, and used to solve a complex problem in water management, namely the optimal design of water distribution systems (including, sizing of components, reliability, renewal and rehabilitation strategies, etc.) using a multi-objective approach. 1. INTRODUCTION Agent Swarm Optimization (ASO) is a generalization of Particle Swarm Optimization, Kennedy and Eberhart [1995], orientated towards distributed artificial intelligence and considering ideas from multi-agent systems. It is aimed at supporting engineering decision-making by solving either single or multi-objective optimization problems. In ASO, one agent is able to find by itself a potential solution for the problem. Nevertheless, the knowledge related to the solution space that agents have is very limited; effective search of optimal solutions is only possible as a result of the interaction among several agents. Every agent has its own individual behaviour. Associations of agents interacting among them result in a collective structure, called swarm, which represents the collective behaviour of a group of agents. This structure can also be considered as an agent in a higher abstraction level. In its turn, each swarm has its own behaviour and is able to interact with other existent swarms. One main difference with multi-agent systems is that
An inverse distance-based potential field function for overlapping point set visualization

In this paper we address the problem of visualizing overlapping sets of points with a fixed positioning in a comprehensible way. A standard visualization technique is to enclose the point sets in isocontours generated by bounding a potential field function. The most commonly used functions are various approximations of the Gaussian distribution. Such an approach produces smooth and appealing shapes, however it may produce an incorrect point nesting in generated regions, e.g. some point is contained inside a foreign set region. We introduce a different potential field function that keeps the desired properties of Gaussian distribution, and in addition guarantees that every point belongs to all its sets' regions and no others, and that regions of two sets with no common points have no overlaps. The presented function works well if the sets intersect each other, a situation that often arises in social network graphs, producing regions that reveal the structure of their clustering.
Post-Retrieval Clustering Using Third-Order Similarity Measures

Post-retrieval clustering is the task of clustering Web search results. Within this context, we propose a new methodology that adapts the classical K-means algorithm to a third-order similarity measure initially developed for NLP tasks. Results obtained with the definition of a new stopping criterion over the ODP-239 and the MORESQUE golden standard datasets evidence that our proposal outperforms all reported text-based approaches.
Two Mechanisms for Simulating Other Minds

People often attempt to understand other minds by using their own thoughts and experiences as a proxy for those of others, a process known broadly as simulation. Recent research in cognitive neuroscience has identified the neural bases of two forms of simulation: mirroring and self-projection. Mirroring involves a vicarious response in which a perceiver experiences the same current mental state as that of another person, and has been linked recently to brain regions that “mirror” the experiential states of others. In contrast, self-projection involves imagining oneself in the same situation as another person, predicting one’s thoughts and feelings in that hypothetical scenario and assuming that the other would think and feel the same way. This form of simulation has been linked to a set of regions known collectively as the default network and includes the medial prefrontal cortex, precuneus and posterior cingulate, and lateral parietal cortex. Although most discussions of simulation have conflated these two processes, here we describe the conceptual and empirical reasons to distinguish between self-projection and mirroring and suggest the unique role each plays in understanding others.
Pairwise Costs in Semisupervised Discriminant Analysis for Face Recognition

In recent years, face recognition is being recognized as a cost-sensitive learning problem. Many cost-sensitive classifiers have been proposed. However, no sufficient attention is paid to the research on cost-sensitive dimensionality reduction, especially on the cost-sensitive semisupervised dimensionality reduction. To the best of our knowledge, cost sensitive semisupervised discriminant analysis (CS3DA) may be the first work. CS3DA first uses the sparse representation to infer a soft label for unlabeled sample and then learns the projection direction by incorporating misclassification costs into both labeled and unlabeled data. Although CS3DA reduces the loss of misclassification, it has two major drawbacks: 1) the sparsity is not a feature of face recognition, and therefore sparse approximations may not deliver the robustness or performance desired and 2) CS3DA is not proven to satisfy the minimal misclassification loss criterion. In this paper, we embed pairwise costs in semisupervised discriminant analysis (PCSDA) for face recognition. PCSDA first uses a simple l2 approach to predict the label of unlabeled data, and then learns the projection direction by embedding pairwise costs in both labeled and unlabeled data. Compared with CS3DA, PCSDA has three major advantages: 1) l2 approach is more accurate and robust than sparse representation for face recognition; 2) we prove that CS3DA approximates the pairwise Bayesian risk only when the classes are balanced and without outliers in face data sets; and 3) PCSDA approximates the pairwise Bayesian risk considering the class imbalance problem and outliers in face recognition. Hence, the projection direction obtained by using PCSDA can be more discriminative, immunes to outliers and class imbalance problem. The experimental results on AR, PIE, ORL, and extended Yale B data sets demonstrate the effectiveness of PCSDA.
$Tack:$ Learning Towards Contextual and Ephemeral Indoor Localization With Crowdsourcing

At events, such as conferences, indoor localization is both contextual and ephemeral, in that localization is only needed within the context of and for the duration of the event. As such, the costs and requirements of providing such services need to be minimal. In this paper, we design, implement, and evaluate Tack, a new mobile application framework that is specifically engineered to support such contextual and ephemeral indoor localization during an event. To provide location-based services with Tack, an event organizer only needs to bring and place a small number of (reusable) beacons around the venue before the event begins. As a system framework, Tack uses a combination of known beacon locations, contacts over bluetooth low energy, crowdsourcing, and dead-reckoning to estimate and refine user locations. To make our location estimates more accurate, we embrace the inherent nature of beacons, design crowdsourcing-based inference algorithms, and present an extensive evaluation by running real-world experiments with iOS devices and beacons. Tack has been implemented as an open-source framework on the iOS platform and can be used by mobile applications designed for events with location-based services.
A Study on Wavelet-based Image Denoising Using a Modified Adaptive Thresholding Method

Thedenoising of a natural image corrupted by Gaussian noise is a long established problem in signal or image processing. Today the research is focus on the wavelet domain, especially using the wavelet threshold method. In this paper, a waveletbased image denoising modified adaptive thresholding method is proposed. The proposed method computes thethreshold adaptively based on the scale level and adaptively estimates wavelet coefficients by using a modified thresholding function that considers the dependency between the parent coefficient and child coefficient and the soft thresholding function at different scales. Experimental results show that the proposed method provides high peak signal-to-noise ratio results and preserves the detailed information of the original image well, resulting in a superior quality image.
Efficient algorithms for subwindow search in object detection and localization

Recently, a simple yet powerful branch-and-bound method called Efficient Subwindow Search (ESS) was developed to speed up sliding window search in object detection. A major drawback of ESS is that its computational complexity varies widely from O(n2) to O(n4) for n × n matrices. Our experimental experience shows that the ESS's performance is highly related to the optimal confidence levels which indicate the probability of the object's presence. In particular, when the object is not in the image, the optimal subwindow scores low and ESS may take a large amount of iterations to converge to the optimal solution and so perform very slow. Addressing this problem, we present two significantly faster methods based on the linear-time Kadane's Algorithm for 1D maximum subarray search. The first algorithm is a novel, computationally superior branch-and-bound method where the worst case complexity is reduced to O(n3). Experiments on the PASCAL VOC 2006 data set demonstrate that this method is significantly and consistently faster (approximately 30 times faster on average) than the original ESS. Our second algorithm is an approximate algorithm based on alternating search, whose computational complexity is typically O(n2). Experiments shows that (on average) it is 30 times faster again than our first algorithm, or 900 times faster than ESS. It is thus well-suited for real time object detection.
The analysis of red giant branch photometry in galaxies

We present an analysis of the many possible methods for simulating and analysing colour–magnitude diagrams, with application to studies of the field halo stellar populations of resolved galaxies. Special consideration is given to the analysis of stars on the red giant branch, and methods for obtaining metallicity distributions for old populations based on analysis of evolved sections of the colour–magnitude diagram. These tools are designed to provide a reliable and accurate method for the analysis of resolved Population II stars in the haloes of nearby galaxies. In particular, we introduce a perpendicular distance method for calculating most likely source isochrones for stars in our input data set. This method is shown to have several advantages over a more traditional approach considering the isochrone as a set of finely spaced Gaussian probability distributions. We also consider methods by which the obtained metallicity distribution might be most efficiently optimized and especially evaluate the difficulties involved in avoiding sub-optimal local maxima in the likelihood maximization procedure.
A Robust Approach to Motion Detection and Tracking in Indoor Video Surveillance

In this paper we propose a robust approach to object detection and tracking for video surveillance. To have a strong and fast pixel classification, an innovative algorithm that uses both frame by frame difference and background subtraction is used for motion detection; tracking is then performed using a Kalman filter that exploits color “blueprint” to perform robust data association. With the proposed algorithm, a correct identification of objects is then possible without a previous background learning or explicit object model. A novel approach to frame by frame blob matching (i.e., data association), named “Relative Maximum Matching” (RMM), based on the comparison between relative maxima of objects histogram, is also presented. Experiments are given with 15 fps tracking of moving people on 640 × 480 unsampled color images.
Looking at the Others : Studies on (un)ethical behavior and social relationships in organizations

markdownabstractThis dissertation asks how social relationships matter for a person’s ethical or unethical behavior in an organization. Two observations motivate this question. First, in organizations, the network of formally prescribed and informally emerging social relationships with others constitutes the distinctive context for the behavior of the individuals. Second, (un)ethical behavior is inherently social in that the consideration of other persons is at the heart of ethics. Four independent studies each answer a specific question derived from this overarching question. The first study explores the role of social relationships in the process of spread of unethical behavior by developing a dynamic actor-oriented social network analysis framework. The second study focuses on the role of social relationships as conduits for information about unethical behavior, and empirically compares observer-reports on unethical behavior to self-reports. The third study empirically analyzes how unethical behavior by peers, which defines the descriptive norms, interacts with the formal rules, which constitute the injunctive norms, to influence individuals’ unethical behavior. The fourth study examines philosophical views which conceive social relationships as the source of ethical obligations to formulate a normative core for an individual-oriented version of stakeholder theory. Taken together, the results of this research show that social relationships can foster as well as impede unethical behavior depending on their specific constellation and on perceptions of their nature; that they are affected by unethical behavior; and that attempts to prevent unethical behavior in organizations are unlikely to be successful unless social relationships are taken into consideration.
SAFEBOX: A Verified Microkernel Based on Spatial-Temporal Isolation

The correctness of kernel is the key to the safety-critical embedded application, and only by formal verification it can prove the kernel does not exist some defects or meet certain attributes. In this paper, we introduce SAFEBOX, a microkernel based on spatial-temporal isolation, give the formal description of SAFEBOX, and use theorem proverb Isabelle/HOL to verify the functional and non-functional properties of SAFEBOX.
A novel compact UWB bandpass filter with quarter-wave short-circuited stubs

This paper proposed a novel compact ultra-wideband (UWB, 3.1GHz-10.6GHz, defined by U.S. Federal Communications Commission) bandpass microstrip filter with λ0/4 short-circuited stub to improve the performance and reduce the size of the UWB filter based on the traditional theory. The proposed structure mainly contains two quarter-wave short-circuited stubs and a compact low-pass filter. The measurements show an insertion loss less than 1.8dB and a return loss above 12dB, the rectangular coefficient of the filter also had been improved and the parasitic passband at twice the center frequency had been suppressed. The dimension of this structure is 8.2mm×5.85mm, which is much less than conventional structures.
Fostering Character Education with Games and Interactive Story Generation

Promoting ethical, responsible, and caring behavior in young people is a perennial aim of education. Schools are invited to include moral teaching in every possible curriculum. Efforts have been made to find non-traditional ways of teaching such as games or role play or engaging students in moral dilemmas. However, classroom environments need to consider time constraints, curriculum standards, and differing children‟s personalities. Computer systems can offer rich environments that detect and respond to student knowledge gaps, misconceptions, and variable affective states. This chapter presents AEINS, an adaptive narrative-based educational game that helps the teaching of basic ethical virtues to young children to promote character education. The central goal is to engage students in a dynamic narrative environment and to involve them in different moral dilemmas (teaching moments) that use the Socratic Method as the predominant pedagogy. We argue that AEINS incorporates appropriate game design principles and successfully manages the interaction between the narrative level and the tutoring level to maximize student learning. Moreover, it is able to convey the moral skills to its users, as shown in the evaluation.
Knowledge Management Technologies and Higher Education Processes: Approach to Integration for Performance Improvement

ABSTRACT The adoption and use of technology to support higher education processes has been increasingly affected by knowledge management (KM), learning technologies and emerging support tools for performance improvement. To achieve their goals, higher education institutions must endeavor to align operational processes and organizational learning with KM technology solutions to create a performance improvement environment that strategically leverages KM technologies with higher education processes. Drawing from the concepts based on processes reengineering in higher education, task-technology fit theory and an integrative learning and performance architecture to form the basis for defining our approach, this paper proposes a theoretical approach for integrating KM technologies in higher education processes with a view to achieving improved service deliveries and sustained competitive advantages. By using the reflections from the above concepts to show the links between KM technologies, higher education processes and performance improvement, this paper does not only give guidance for integration of KM technologies in higher education processes, but also act as a catalyst for optimizing higher education process modeling development.
Inside-out and outside-in: Applying the concept of conventions in the analysis of policy implementation through sport clubs

The aim of this article is to enrich theoretically the analysis of processes of policy implementation through sport clubs. Subsequent to reviewing previous theoretical contributions on this topic, we make the case that available conceptualizations are marked by an inside-out perspective and that they conceptually and empirically stop short at the end implementer, i.e. the sport club. Consequently, analyses of policy implementation through sport clubs have not taken into account the fact that sport clubs are distinctly local phenomena. As such, past, current and potential future participants, volunteers and local inter-organizational relationships are found in a sport club’s local community. Because of this, there is a need for a concept that provides analytical coverage of an outside-in perspective, i.e. a concept that takes into account outside actors’ conceptions of the implementing sport club and the impact their views have on the implementation process. In relation to this need, we propose the application of the concept ‘convention’. In addition to describing the concept, we exemplify the methodological and analytical implications of its use in sport policy implementation analysis.