Miracle-dz/UniFAR-FTUs huggingface dataset:
Proceedings of the Workshop of Making as a Pathway to Foster Joyful Engagement and Creativity in Learning ( Make 2 Learn )

The International Workshop of Making as a Pathway to Foster Joyful Engagement and Creativity in Learning (Make2Learn) aims to discuss the introduction of creative and joyful production of artifacts “maker movement” in the learning processes. A variety of environments have been developed by researchers to introduce making principles to young students. Making principles enable them foster co-creativity and joy in learning processes and construct knowledge. By involving students in the design decisions they begin to develop technological fluency and the needed competences, in a joyful way. Make2Learn aims to bring together international researchers, educators, designers, and makers for the exploration of making principles towards the acquisition of 21st Century learning competences, by employing the state art aspects of entertainment technologies, new media, gaming, robotics, toys and applications. The main objective is to build a research community around this topical area. In particular, Make2Learn aims to develop a critical discussion about the well-established practices and entertainment technologies of the maker movement, and expected outcomes of putting them into practice under different spaces such as Hackerspaces, Makerspaces, TechShops, FabLabs etc. This will allow us to better understand and improve the value of Maker philosophy and the role of entertainment technologies to support teaching and learning.
Fast algorithm for Multiple-Circle detection on images using Learning Automata

Hough transform has been the most common method for circle detection exhibiting robustness but adversely demanding a considerable computational load and large storage. Alternative approaches include heuristic methods that employ iterative optimisation procedures for detecting multiple circles under the inconvenience that only one circle can be marked at each optimisation cycle demanding a longer execution time. In contrast, learning automata (LA) is a heuristic method to solve complex multi-modal optimisation problems. Although LA converges to just one global minimum, the final probability distribution holds valuable information regarding other local minima which have emerged during the optimisation process. The detection process is considered as a multi-modal optimisation problem, allowing the detection of multiple circular shapes through only one optimisation procedure. The algorithm uses a combination of three edge points as parameters to determine circles candidates. A reinforcement signal determines whether such circle candidates are actually present at the image. Guided by the values of such reinforcement signal, the set of encoded candidate circles are evolved using the LA so that they can fit into actual circular shapes over the edge-only map of the image. The overall approach is a fast multiple-circle detector despite facing complicated conditions.
Crowdsourcing and Creativity

In studying creativity and techniques for enhancing the creative process, we utilized crowdsourcing through Amazon’s Mechanical Turk. Through our studies we found that in its current form, the Mechanical Turk platform has some limitations in supporting creative tasks. In this position paper, we describe our studies and give recommendations for crowd-sourcing platforms that foster creativity.
Kinematic modelling of cable wrapping on rigid-link phenomenon in cable-driven parallel manipulators

In cable-driven parallel manipulators, the interference between the cable and the rigid link(s) of the mechanisms were generally excluded from the workspace consideration. This leads to the loss of perfectly feasible workspace of the mechanism. In this paper, the kinematic modelling of the cable path in a cable-driven parallel manipulator which involves a segment of the cable wrapped over the surface of the rigid link, is presented. The wrapping of the cable is dependent on the displacement of the rigid link and the contact between the cable and the rigid link will no longer be restricted to the cable attachment point (as in the case of a conventional cable-driven parallel manipulator), but could be extended to a line on the surface of the rigid link (ie. cable wrapping over the rigid link). It is assumed that the cable remains taut at all time, therefore finding the cable configuration is equivalent to finding a geodesic solution of a convex hull of a rigid body. The analysis is extended from finding the solution to the path of a single cable wrapped over an arbitrary rigid body to address a cable-driven parallel manipulator that allows wrapped cable segments in its operation.
Performance Evaluation of the . Accrual Failure Detector

In this paper, we explain an implementation of an accrual failure detector, that we call the . failure detector. The particularity of the . failure detector is that it dynamically adjusts to current network conditions the scale on which the suspicion level is expressed. We have done the experiment in a LAN in a whole day and evaluated the behavior of our . failure detector. Then we discuss on the parameters of the failure detector based on our experimental result.
Ranking Aggregation Based on Belief Function Theory

The ranking aggregation problem is that to establishing a new aggregate ranking given a set of rankings of a finite set of items. This problem is met in various applications, such as the combination of user preferences, the combination of lists of documents retrieved by search engines and the combination of ranked gene lists. In the literature, the ranking aggregation problem has been solved as an optimization of some distance between the rankings overlooking the existence of a true ranking. In this thesis we address the ranking aggregation problem assuming the existence of a true ranking on the set of items: the goal is to estimate an unknown, true ranking given a set of input rankings provided by experts with different approximation quality. We propose a novel solution called Belief Ranking Estimator (BRE) that takes into account two aspects still unexplored in ranking combination: the approximation quality of the experts and for the first time the uncertainty related to each item position in the ranking. BRE estimates in an unsupervised way the true ranking given a set of rankings that are diverse quality estimations of the unknown true ranking. The uncertainty on the items's position in each ranking is modeled within the Belief Function Theory framework, that allows for the combination of subjective knowledge in a non Bayesian way. This innovative application of belief functions to rankings, allows us to encode different sources of a priori knowledge about the correctness of the ranking positions and also to weigh the reliability of the experts involved in the combination. We assessed the performance of our solution on synthetic and real data against state-of-the-art methods. The tests comprise the aggregation of total and partial rankings in different empirical settings aimed at representing the different quality of the input rankings with respect to the true ranking. The results show that BRE provides an effective solution when the input rankings are heterogeneous in terms of approximation quality with respect to the unknown true ranking.
Generation of Neural Progenitor Cells from Whole Adult Bone Marrow

The efficient and large-scale generation of neural progenitor cells for neural grafting in the treatment of neurological diseases has been a challenge. Here we describe the isolation and successful propagation of neural progenitor cells from adult rat bone marrow. Unfractionated bone marrow cultured in vitro with epidermal growth factor and basic fibroblast growth factor gave rise to cellular spheres which differentiated into neurons and glia. The cellular spheres expressed nestin, a neural stem cell marker as well as CD90, a marker of hematopoietic stem cells. This methodology addresses the ethical and tissue rejection problems associated with fetal neural stem cells and would circumvent the difficulty associated with generating neural progenitors from the adult brain. We demonstrate that bone marrow may offer a renewable autologous extracranial source of neural progenitor cells.
The Verlinde algebra is twisted equivariant K-theory

The Verlinde algebra, which mathematically appears in the theory of loop groups and is related to moduli spaces of bundles over curves, turns out to be describable in terms of K-theory. This is a joint discovery with M. Hopkins and C. Teleman. Here we explain in heuristic terms how this fits naturally into ideas about the Chern-Simons topological field theory.
Models@ run.time

Runtime adaptation mechanisms that leverage software models extend the applicability of model-driven engineering techniques to the runtime environment. Contemporary mission-critical software systems are often expected to safely adapt to changes in their execution environment. Given the critical roles these systems play, it is often inconvenient to take them offline to adapt their functionality. Consequently, these systems are required, when feasible, to adapt their behavior at runtime with little or no human intervention. A promising approach to managing complexity in runtime environments is to develop adaptation mechanisms that leverage software models, referred to as models@run. time. Work on models@run.time seeks to extend the applicability of models produced in model-driven engineering (MDE) approaches to the runtime environment. Models@run. time is a causally connected self-representation of the associated system that emphasizes the structure, behavior, or goals of the system from a problem space perspective.
Distributed control plane for software-defined networks: a case study using event-based middleware

Realizing a communication middleware in a software-defined network can leverage significant performance gains in terms of latency, throughput and bandwidth efficiency. For example, filtering operations in an event-based middleware can be performed highly efficiently in the TCAM memory of switches enabling line-rate forwarding of events. A key challenge in a software-defined network, however, is to ensure high responsiveness of the control plane to dynamically changing communication interactions. In this paper, we propose a methodology for both vertical and horizontal scaling of the distributed control plane that is capable of improving the responsiveness by enabling concurrent network updates in the presence of high dynamics while ensuring consistent changes to the data plane of a communication middleware. In contrast to existing scaling approaches that aim for a general-purpose distributed control plane, our approach uses knowledge of the application semantics that is already available in the design of the data plane of a communication middleware, e.g. subscriptions and advertisements in an event-based middleware. By proposing a methodology for an application-aware control distribution, we show, in the context of PLEROMA, an event-based middleware, that application-awareness is significantly beneficial in avoiding the synchronization bottlenecks for ensuring consistency in the presence of concurrent network updates and thus greatly improves the responsiveness of the control plane.
BCS-FACS 7 th Refinement Workshop Proceedings of the BCS-FACS 7 th Refinement Workshop , Bath , 3-5 July 1996 A Tool for Developing Correct Programs by Refinement

The refinement calculus for the development of programs from specifications is well suited to mechanised support. We review the requirements for tool support of refinement as gleaned from our experience with a number of existing refinement tools, and report on the design and implementation of a new tool to support refinement based on these requirements. The main features of the new tool are close integration of refinementand proof in a single tool (the same mechanism is used for both), good management of the refinement context, an extensible theory base that allows the tool to be adapted to new application domains, and a flexible user interface.
Optimal Transaction Filters Under Transitory Trading Opportunities: Theory and Empirical Illustration

If transitory profitable trading opportunities exist, filter rules are used to mitigate transaction costs. We use a dynamic programming framework to design an optimal filter which maximizes after-cost expected returns. The filter size depends crucially on the degree of persistence of trading opportunities, transaction cost, and standard deviation of shocks. Applying our theory to daily dollar-yen exchange trading, we find that the optimal filter can be economically significantly different from a naive filter equal to the transaction cost. The candidate trading strategies generate positive returns that disappear after accounting for transaction costs. However, when the optimal filter is used, returns after costs remain positive and are higher than for naive filters.
The 18 Th European Conference on Machine Learning and the 11 Th European Conference on Principles and Practice of Knowledge Discovery in Databases Proceedings of the Ecml/pkdd'2007 Discovery Challenge Ecml/pkdd'2007 Discovery Challenge Organization Program Committee Effective Prediction of Web User 

Preface Knowledge discovery in real-world databases requires a broad scope of techniques and forms of knowledge. Both the knowledge and the applied methods should fit the discovery tasks and should adapt to knowledge hidden in the data. The Discovery Challenge will encourage a collaborative research effort, a broad and unified view of knowledge and methods of discovery, and emphasis on business problems and solutions to those problems. The idea of Discovery Challenge came from Jan ˙ Zytkow, who suggested to organize such an event during PKDD'99 in Prague. The Discovery Challenge constitutes a collection of data and problems as a common ground for better comparisons and discussions of the applicability of KDD methods on a real-world problems with respect to both KDD and application viewpoints. The main goals of the Discovery Challenge are – stimulate an open view of knowledge and discovery – stimulate collaborative approach to KDD and research on unification of both different forms of knowledge and discovery – integrate into KDD an emphasis on real-world problems and solutions to those problems This year's Discovery Challenge was devoted to three problems: user behaviour ptrediction from web traffic logs, HTTP traffic classification, and Sumerian literature understanding. 1. User's behaviour prediction This task is co-organized by Gemius, the leading Internet market research company in Poland. The problem objective is to predict user behaviour by characterising nature of user's visit, i.e., the list categories of the visited Internet portal and the number of page views in each category. The challenge is accomplished with use of web traffic data from Polish web sites employing gemiusTraffic study, grouped by appropriate categories. 2. HTML traffic prediction The task is co-organized by the LIRMM (Laboratoire d'Informatique, de Robo-tique et de Microélectronique de Montpellier, FRANCE) and the LGI2P (Ecole des Mines d'Alčs, FRANCE) and is based on a dataset of real-world web traffic in conjunction with Bee Ware, a leading provider of secure web enabled delivery solutions. The aim of this contest is to classify HTTP traffic into multiple classes, distinguishing between different attack types but also between anomalous and normal traffic. The procedure involves 3 tasks respectively handling classification, pattern isolation and performance issues. 3. Sumerian Literature understanding The challenge is related to a database of ca. 28'000 administrative documents from the kingdom of the III Dynasty of Ur, which existed in the 21st century b.C. in Mesopotamia, present day southern Iraq. The documents were …
Impression Formation in Computer-Mediated Communication and Making a Good (Virtual) Impression

As the saying goes, you never get a second chance to make a good first impression. This is especially true when working virtually, where impressions are formed via computer-mediated communication (CMC). As Wallace observes, “Increasingly.the online persona is playing a larger role in first impressions as people rely on email, websites, and discussion forums more for the first contact, and the phone call, letter, or face-to-face meetings less” (1999, p. 14). The varieties of nonverbal cues that normally aid in impression formation do not exist in an online environment. In the absence of these nonverbal cues, forming impressions via CMC places a greater emphasis on verbal (text-based) and linguistic cues, as well as depending more upon social cues such as shared schema, context, and stereotypes. Indeed, as Tannis and Postmes observe, “communications over the Internet are all but free from influences of the social, the cognitive, and the physical” (2003, p. 692). It is critical, then, that people working virtually understand how impressions are formed in an online environment and what types of cues aid AbstrAct
Exploring the tradeoff between power and detection rate for a face detection algorithm

Human face detection plays an important role in many applications such as human computer interface, video surveillance, face recognition, and face image database management. Recently, most researches mainly focus on developing a high detection rate algorithm to improve the performance. Since a high performance face detection algorithm requires complicated framework, it results high energy consumption and hard to implement in portable devices whose battery lifes are always restricted. Therefore, how to develop a face detection algorithm with both promising detection rate and low power dissipation has become a crucial research topic. In this paper, we present a comprehensive analysis of energy requirements in a wide range of the most used algorithms in the face detection program. The experimental results show the power dissipation and detection rate of seven different cases. Base on the results, we conclude and discuss various opportunities for realizing energy-efficient implementations of a face detection program.
CrossVit: Enhancing Canopy Monitoring Management Practices in Viticulture

A new wireless sensor network (WSN), called CrossVit, and based on MEMSIC products, has been tested for two growing seasons in two vineyards in Italy. The aims are to evaluate the monitoring performances of the new WSN directly in the vineyard and collect air temperature, air humidity and solar radiation data to support vineyard management practices. The WSN consists of various levels: the Master/Gateway level coordinates the WSN and performs data aggregation; the Farm/Server level takes care of storing data on a server, data processing and graphic rendering; Nodes level is based on a network of peripheral nodes consisting of a MDA300 sensor board and Iris module and equipped with thermistors for air temperature, photodiodes for global and diffuse solar radiation, and an HTM2500LF sensor for relative humidity. The communication levels are: WSN links between gateways and sensor nodes by ZigBee, and long-range GSM/GPRS links between gateways and the server farm level. The system was able to monitor the agrometeorological parameters in the vineyard: solar radiation, air temperature and air humidity, detecting the differences between the canopy treatments applied. The performance of CrossVit, in terms of monitoring and reliability of the system, have been evaluated considering: its handiness, cost-effective, non-invasive dimensions and low power consumption.
Non-local mind from the perspective of social cognition

Two main conceptual approaches have been employed to study the mechanisms of social cognition, whether one considers isolated or interacting minds. Using neuro-imaging of subjects in isolation, the former approach has provided knowledge on the neural underpinning of a variety of social processes. However, it has been argued that considering one brain alone cannot account for all mechanisms subtending online social interaction. This challenge has been tackled recently by using neuro-imaging of multiple interacting subjects in more ecological settings. The present short review aims at offering a comprehensive view on various advances done in the last decade. We provide a taxonomy of existing research in neuroscience of social interaction, situating them in the frame of general organization principles of social cognition. Finally, we discuss the putative enabling role of emerging non-local social mechanisms—such as interpersonal brain and body coupling—in processes underlying our ability to create a shared world.
COLLABORATION AND INTERACTION AS THE MAINSTAYS OF AN IMPROVED LEARNING MANAGEMENT SYSTEM

There are many commercially available online class management systems (OCMS) or learning management systems (LMS). Most have been developed to manage the data for a class and to track the participants’ activities. Very few have centered their efforts on making the environment participant friendly. Even fewer have sough to create an environment where the participant is encouraged to be an informed contributor for the community. Most systems are designed so that the participant merely logs into the system to learn when an assignment is due, download an assignment or other information, take a survey, quiz, or test, and then log off the system. This is a failure to foster a constructive learning environment. The following paper describes the development and maturation of an OCMS that was engineered designed to be participant centric, to allow a large degree of interactivity, and to allow participant ownership of information within the system. Many of the features incorporated in the system give the participant reasons to remain engaged and even to contribute to the system ‐ as opposed to merely lurking in the background. The “informal and reciprocal knowledge exchanges between individuals are valued and sustained over time because the sharing of knowledge is an important aspect of being a member of a technological community” [8]. We developed an innovative system entitled “Community/course Action/interaction Management System” (CAMS). The link to it (http://cams-community.com/cams/trial_entry.php) will automatically log you into a demonstration community called Class Example. It allows up to thirty-one people to enter the community at the same time and try out the system. This demonstration community gives each person a distinctive identity and even includes a unique fake picture for each participant.
SOLVING GENERAL CONVEX QP PROBLEMS VIA AN EXACT QUADRATIC AUGMENTED LAGRANGIAN WITH BOUND CONSTRAINTS

Large convex quadratic programs, where constraints are of box type only, can be solved quite eeciently 1], 2], 12], 13], 16]. In this paper an exact quadratic augmented Lagrangian with bound constraints is constructed which allows one to use these methods for general constrained convex quadratic programming. This is in contrast to well known exact diierentiable penalty functions for this type of problem, which are not quadratic, e.g. 7], 10], 15].
ON TRUST IN EMPLOYMENT RELATIONSHIPS - THE CASE OF CHILD-MINDERS

In many employment relationships, the employer and the employee are both vulnerable. The vulnerabilities can be alleviated if both parties agree to trust each other and not to harm each other. Following Mauss and Lindenberg, in the article a framework is developed to analyze trust in employment relationships. It is argued that to gain the trust of the employee, employers make relational signals, and that the employee accepts these signals by showing motivation, commitment, loyalty and endurance in his job. From this framework, hypotheses have been derived about wages and allocation in the informal labour market of child-minders. It is demonstrated that employers with high incomes use monetary rewards to govern the employment relationship, especially with better educated child-minders. Strong indications have been found that employers with low incomes use other means, such as homogeneity and social exchange, to bind their child-minder.