An Editor for Helping Novices to Learn Standard ML

This paper describes a novel editor intended as an aid in the learning of the functional programming language Standard ML. A common technique used by novices is programming by analogy whereby students refer to similar programs that they have written before or have seen in the course literature and use these programs as a basis to write a new program. We present a novel editor for ML which supports programming by analogy by providing a collection of editing commands that transform old programs into new ones. Each command makes changes to an isolated part of the program. These changes are propagated to the rest of the program using analogical techniques. We observed a group of novice ML students to determine the most common programming errors in learning ML and restrict our editor such that it is impossible to commit these errors. In this way, students encounter fewer bugs and so their rate of learning increases. Our editor, C Y NTHIA, has been implemented and is due to be tested on st..

A computational fluid dynamic investigation of inhomogeneous hydrogen flame acceleration and transition to detonation

Gas explosions in homogeneous reactive mixtures have been widely studied both experimentally and numerically. However, in practice and industrial applications, combustible mixtures are usually inhomogeneous and subject to vertical concentration gradients. Limited studies have been conducted in such context which resulted in limited understanding of the explosion characteristics in such situations. The present numerical investigation aims to study the dynamics of Deflagration to Detonation Transition (DDT) in inhomogeneous hydrogen/air mixtures and examine the effects of obstacle blockage ratio in DDT. VCEFoam, a reactive density-based solver recently assembled by the authors within the frame of OpenFOAM CFD toolbox has been used. VCEFoam uses the Harten–Lax–van Leer–Contact (HLLC) scheme fr the convective fluxes contribution and shock capturing. The solver has been verified by comparing its predictions with the analytical solutions of two classical test cases. For validation, the experimental data of Boeck et al. (1) is used. The experiments were conducted in a rectangular channel the three different blockage ratios and hydrogen concentrations. Comparison is presented between the predicted and measured flame tip velocities. The shaded contours of the predicted temperature and numerical Schlieren (magnitude of density gradient) will be analysed to examine the effects of the blockage ratio on flame acceleration and DDT

The future of social is personal: the potential of the personal data store

This chapter argues that technical architectures that facilitate the longitudinal, decentralised and individual-centric personal collection and curation of data will be an important, but partial, response to the pressing problem of the autonomy of the data subject, and the asymmetry of power between the subject and large scale service providers/data consumers. Towards framing the scope and role of such Personal Data Stores (PDSes), the legalistic notion of personal data is examined, and it is argued that a more inclusive, intuitive notion expresses more accurately what individuals require in order to preserve their autonomy in a data-driven world of large aggregators. Six challenges towards realising the PDS vision are set out: the requirement to store data for long periods; the difficulties of managing data for individuals; the need to reconsider the regulatory basis for third-party access to data; the need to comply with international data handling standards; the need to integrate privacy-enhancing technologies; and the need to future-proof data gathering against the evolution of social norms. The open experimental PDS platform INDX is introduced and described, as a means of beginning to address at least some of these six challenges

An analysis of the cost and benefit of search interactions

Interactive Information Retrieval (IR) systems often provide various features and functions, such as query suggestions and relevance feedback, that a user may or may not decide to use. The decision to take such an option has associated costs and may lead to some benefit. Thus, a savvy user would take decisions that maximises their net benefit. In this paper, we formally model the costs and benefits of various decisions that users, implicitly or explicitly, make when searching. We consider and analyse the following scenarios: (i) how long a user's query should be? (ii) should the user pose a specific or vague query? (iii) should the user take a suggestion or re-formulate? (iv) when should a user employ relevance feedback? and (v) when would the "find similar" functionality be worthwhile to the user? To this end, we build a series of cost-benefit models exploring a variety of parameters that affect the decisions at play. Through the analyses, we are able to draw a number of insights into different decisions, provide explanations for observed behaviours and generate numerous testable hypotheses. This work not only serves as a basis for future empirical work, but also as a template for developing other cost-benefit models involving human-computer interaction

Information scent, searching and stopping : modelling SERP level stopping behaviour

Current models and measures of the \emph{Interactive Information Retrieval (IIR)} process typically assume that a searcher will always examine the first snippet in a given \emph{Search Engine Results Page (SERP)}, and then with some probability or cutoff, he or she will stop examining snippets and/or documents in the ranked list (snippet level stopping). Prior work has however shown that searchers will form an initial impression of the SERP, and will often abandon a page without clicking on or inspecting in detail any snippets or documents. That is, the \emph{information scent} affects their decision to continue. In this work, we examine whether considering the information scent of a page leads to better predictions of stopping behaviour. In a simulated analysis, grounded with data from a prior user study, we show that introducing a SERP level stopping strategy can improve the performance attained by simulated users, resulting in an increase in gain across most snippet level stopping strategies. When compared to actual search and stopping behaviour, incorporating SERP level stopping offers a closer approximation than without. These findings show that models and measures that na\"{i}vely assume snippets and documents in a ranked list are actually examined in detail are less accurate, and that modelling SERP level stopping is required to create more realistic models of the search process

RIP1-HAT1-SirT complex identification and targeting in treatment and prevention of cancer

Purpose: Alteration in cell death is a hallmark of cancer. A functional role regulating survival, apoptosis, and necroptosis has been attributed to RIP1/3 complexes.Experimental Design: We have investigated the role of RIP1 and the effects of MC2494 in cell death induction, using different methods as flow cytometry, transcriptome analysis, immunoprecipitation, enzymatic assays, transfections, mutagenesis, and in vivo studies with different mice models.Results: Here, we show that RIP1 is highly expressed in cancer, and we define a novel RIP1/3-SIRT1/2-HAT1/4 complex. Mass spectrometry identified five acetylations in the kinase and death domain of RIP1. The novel characterized pan-SIRT inhibitor, MC2494, increases RIP1 acetylation at two additional sites in the death domain. Mutagenesis of the acetylated lysine decreases RIP1-dependent cell death, suggesting a role for acetylation of the RIP1 complex in cell death modulation. Accordingly, MC2494 displays tumor-selective potential in vitro, in leukemic blasts ex vivo, and in vivo in both xenograft and allograft cancer models. Mechanistically, MC2494 induces bona fide tumor-restricted acetylated RIP1/caspase-8-mediated apoptosis. Excitingly, MC2494 displays tumor-preventive activity by blocking 7,12-dimethylbenz(α)anthracene-induced mammary gland hyperproliferation in vivoConclusions: These preventive features might prove useful in patients who may benefit from a recurrence-preventive approach with low toxicity during follow-up phases and in cases of established cancer predisposition. Thus, targeting the newly identified RIP1 complex may represent an attractive novel paradigm in cancer treatment and prevention

Evaluating implicit feedback models using searcher simulations

In this article we describe an evaluation of relevance feedback (RF) algorithms using searcher simulations. Since these algorithms select additional terms for query modification based on inferences made from searcher interaction, not on relevance information searchers explicitly provide (as in traditional RF), we refer to them as implicit feedback models. We introduce six different models that base their decisions on the interactions of searchers and use different approaches to rank query modification terms. The aim of this article is to determine which of these models should be used to assist searchers in the systems we develop. To evaluate these models we used searcher simulations that afforded us more control over the experimental conditions than experiments with human subjects and allowed complex interaction to be modeled without the need for costly human experimentation. The simulation-based evaluation methodology measures how well the models learn the distribution of terms across relevant documents (i.e., learn what information is relevant) and how well they improve search effectiveness (i.e., create effective search queries). Our findings show that an implicit feedback model based on Jeffrey's rule of conditioning outperformed other models under investigation

S-COL: A Copernican turn for the development of flexibly reusable collaboration scripts

Collaboration scripts are usually implemented as parts of a particular collaborative-learning platform. Therefore, scripts of demonstrated effectiveness are hardly used with learning platforms at other sites, and replication studies are rare. The approach of a platform-independent description language for scripts that allows for easy implementation of the same script on different platforms has not succeeded yet in making the transfer of scripts feasible. We present an alternative solution that treats the problem as a special case of providing support on top of diverse Web pages: In this case, the challenge is to trigger support based on the recognition of a Web page as belonging to a specific type of functionally equivalent pages such as the search query form or the results page of a search engine. The solution suggested has been implemented by means of a tool called S-COL (Scripting for Collaborative Online Learning) and allows for the sustainable development of scripts and scaffolds that can be used with a broad variety of content and platforms. The tool’s functions are described. In order to demonstrate the feasibility and ease of script reuse with S-COL, we describe the flexible re-implementation of a collaboration script for argumentation in S-COL and its adaptation to different learning platforms. To demonstrate that a collaboration script implemented in S-COL can actually foster learning, an empirical study about the effects of a specific script for collaborative online search on learning activities is presented. The further potentials and the limitations of the S-COL approach are discussed

Informavores: Active information foraging and human cognition

Just as the body survives by ingesting negative entropy, so the mind survives by ingesting information. In a very general sense, all higher organisms are informavores. The study of active information search is in the midst of a renaissance. Psychological research from diverse areas ranging from developmental psychology This symposium aims to bring together leading experts in this area to discuss how active information foraging can be understood from a diverse set of perspectives within cognitive science. Key themes include how prior knowledge influences search (Markant & Gureckis), how information and reward interact to determine choice (Meder & Nelson), developmental patterns in information seeking behavior (Nelson et al.), information foraging in complex sensemaking tasks (Pirolli), and the allocation of attention during statistical word learning (Yu). While each represents a distinct area of research, all discussants in the symposium share a core approach of applying computational models to understand information search in humans. The symposium should appeal to a broad set of attendees including educators, developmental psychologists, cognitive modelers, and computer scientists. The influence of priors on sequential search decisions - Doug Markant and Todd Gureckis Normative models of information acquisition predict that people's search decisions should be strongly influenced by their prior beliefs, which capture the set of alternative hypotheses they are considering. In the present experiments we tested whether people adjusted their information search behavior in response to sequential changes in the prior. Participants played a search game in which they had to identify the shape and location of multiple hidden targets in a display (similar to the board game Battleship). During the task they were told that the set of possible shapes had changed, and the key question was whether they would adjust their search decisions according to the predictions of a normative model. Manipulations of the prior included changes in the frequency of certain classes of targets as well as the introduction of higherorder constraints (e.g., that all targets would have the same shape). The results showed that an individual's prior could be recovered from their sequences of search decisions, but that there were notable differences in their ability to adjust to certain changes in the hypothesis space, an effect that is not predicted by the normative model. We discuss the implications of these findings for how people generate and represent hypotheses during the course of information foraging. Is people's information search behavior sensitive to different reward structures? -Björn Meder and Jonathan Nelson In situations where humans actively acquire information for classification, information search preferentially maximizes accurac