Universally composable contributory group key exchange

We treat the security of group key exchange (GKE) in the universal composability (UC) framework. Analyzing GKE protocols in the UC framework naturally addresses attacks by malicious insiders. We define an ideal functionality for GKE that captures contributiveness in addition to other desired security goals. We show that an efficient two-round protocol securely realizes the proposed functionality in the random oracle model. As a result, we obtain the most efficient UC-secure contributory GKE protocol known.

Modeling key compromise impersonation attacks on group key exchange protocols

A key exchange protocol allows a set of parties to agree upon a secret session key over a public network. Two-party key exchange (2PKE) protocols have been rigorously analyzed under various models considering different adversarial actions. However, the analysis of group key exchange (GKE) protocols has not been as extensive as that of 2PKE protocols. Particularly, the security attribute of key compromise impersonation (KCI) resilience has so far been ignored for the case of GKE protocols. We first model the security of GKE protocols addressing KCI attacks by both outsider and insider adversaries. We then show that a few existing protocols are not secure even against outsider KCI attacks. The attacks on these protocols demonstrate the necessity of considering KCI resilience for GKE protocols. Finally, we give a new proof of security for an existing GKE protocol under the revised model assuming random oracles.

Context and models for the analysis of individual and group needs

This paper summarizes the papers presented in the thematic stream Models for the Analysis of Individual and Group Needs, at the 2007 IAEVG-SVP-NCDA Symposium: Vocational Psychology and Career Guidance Practice: An International Partnership. The predominant theme which emerged from the papers was that theory and practice need to be positioned within their contexts. For this paper, context has been formulated as a dimension ranging from the individual’s experience of himself or herself in conversations, including interpersonal transactions and body culture, through to broad higher levels of education, work, nation, and economy.

Faster group operations on elliptic curves

This paper improves implementation techniques of Elliptic Curve Cryptography. We introduce new formulae and algorithms for the group law on Jacobi quartic, Jacobi intersection, Edwards, and Hessian curves. The proposed formulae and algorithms can save time in suitable point representations. To support our claims, a cost comparison is made with classic scalar multiplication algorithms using previous and current operation counts. Most notably, the best speeds are obtained from Jacobi quartic curves which provide the fastest timings for most scalar multiplication strategies benefiting from the proposed 12M + 5S + 1D point doubling and 7M + 3S + 1D point addition algorithms. Furthermore, the new addition algorithm provides an efficient way to protect against side channel attacks which are based on simple power analysis (SPA). Keywords: Efficient elliptic curve arithmetic,unified addition, side channel attack.

Group metacognition during mathematical problem solving

Although various studies have shown that groups are more productive than individuals in complex mathematical problem solving, not all groups work together cooperatively. This review highlights that addressing organisational and cognitive factors to help scaffold group mathematical problem solving is necessary but not sufficient. Successful group problem solving also needs to incorporate metacognitive factors in order for groups to reflect on the organisational and cognitive factors influencing their group mathematical problem solving.

A participative education program to reduce speeding in a group of work-related drivers

A number of intervention approaches have been developed to improve work-related driving safety. However, past interventions have been limited in that they have been data-driven, and have not been developed within a theoretical framework. The aim of this study is to present a theory-driven intervention. Based on the methodology developed by Ludwig and Geller (1991), this study evaluates the effectiveness of a participative education intervention on a group of work-related drivers (n = 28; experimental group n = 19, control n = 9). The results support the effectiveness of the intervention in reducing speeding over a six month period, while a non significant increase was found in the control group. The results of this study have important implications for organisations developing theory-driven interventions designed to improve work-related driving behaviour.

Report on Innovation Working Group : The Mathematics Education into the 21st Century Project. Dresden, Saxony

This document reports on the Innovations Working Group that met at the 10th International Conference “Models in Developing Mathematics Education” from the 11-17th September 2009 in Dresden, Saxony. It briefly describes the over arching and consistent themes that emerged from the numerous papers presented. The authors and titles of each of the papers presented will be listed in Table 2.

Crowd Counting Using Multiple Local Features

In public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels can be detected using holistic image features, however this requires a large amount of training data to capture the wide variations in crowd distribution. If a crowd counting algorithm is to be deployed across a large number of cameras, such a large and burdensome training requirement is far from ideal. In this paper we propose an approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes. This results in an approach that is scalable to crowd volumes not seen in the training data, and can be trained on a very small data set. As a local approach is used, the proposed algorithm can easily be used to estimate crowd density throughout different regions of the scene and be used in a multi-camera environment. A unique localised approach to ground truth annotation reduces the required training data is also presented, as a localised approach to crowd counting has different training requirements to a holistic one. Testing on a large pedestrian database compares the proposed technique to existing holistic techniques and demonstrates improved accuracy, and superior performance when test conditions are unseen in the training set, or a minimal training set is used.

Writing together, learning together : the value and effectiveness of a research writing group for doctoral students

The high level of scholarly writing required for a doctoral thesis is a challenge for many research students. However, formal academic writing training is not a core component of many doctoral programs. Informal writing groups for doctoral students may be one method of contributing to the improvement of scholarly writing. In this paper, we report on a writing group that was initiated by an experienced writer and higher degree research supervisor to support and improve her doctoral students’ writing capabilities. Over time, this group developed a workable model to suit their varying needs and circumstances. The model comprised group sessions, an email group, and individual writing. Here, we use a narrative approach to explore the effectiveness and value of our research writing group model in improving scholarly writing. The data consisted of doctoral students’ reflections to stimulus questions about their writing progress and experiences. The stimulus questions sought to probe individual concerns about their own writing, what they had learned in the research writing group, the benefits of the group, and the disadvantages and challenges to participation. These reflections were analysed using thematic analysis. Following this analysis, the supervisor provided her perspective on the key themes that emerged. Results revealed that, through the writing group, members learned technical elements (e.g., paragraph structure), non-technical elements (e.g., working within limited timeframes), conceptual elements (e.g., constructing a cohesive arguments), collaborative writing processes, and how to edit and respond to feedback. In addition to improved writing quality, other benefits were opportunities for shared writing experiences, peer support, and increased confidence and motivation. The writing group provides a unique social learning environment with opportunities for: professional dialogue about writing, peer learning and review, and developing a supportive peer network. Thus our research writing group has proved an effective avenue for building doctoral students’ capability in scholarly writing. The proposed model for a research writing group could be applicable to any context, regardless of the type and location of the university, university faculty, doctoral program structure, or number of postgraduate students. It could also be used within a group of students with diverse research abilities, needs, topics and methodologies. However, it requires a group facilitator with sufficient expertise in scholarly writing and experience in doctoral supervision who can both engage the group in planned writing activities and also capitalise on fruitful lines of discussion related to students’ concerns as they arise. The research writing group is not intended to replace traditional supervision processes nor existing training. However it has clear benefits for improving scholarly writing in doctoral research programs particularly in an era of rapidly increasing student load.

Prediction of outcome with group cognitive therapy for depression

Tested a social–cognitive model of depressive episodes and their treatment within a predictive study of treatment response. 42 clinically depressed volunteers (aged 22–60 yrs) were given self-efficacy (SE) questionnaires and other measures before and after treatment with cognitive therapy. Results support the idea that SE and skills regarding control of negative cognition mediates a sustained response to cognitive treatment for depression. Not only did mood-control variables correlate highly with concurrent changes in depression scores during treatment, but the posttreatment SE measure discriminated Ss who relapsed over the next 12 mo.

Word Segmentation for Chinese Wikipedia Using N-Gram Mutual Information

In this paper, we propose an unsupervised segmentation approach, named "n-gram mutual information", or NGMI, which is used to segment Chinese documents into n-character words or phrases, using language statistics drawn from the Chinese Wikipedia corpus. The approach alleviates the tremendous effort that is required in preparing and maintaining the manually segmented Chinese text for training purposes, and manually maintaining ever expanding lexicons. Previously, mutual information was used to achieve automated segmentation into 2-character words. The NGMI approach extends the approach to handle longer n-character words. Experiments with heterogeneous documents from the Chinese Wikipedia collection show good results.

Primary students' group metacognitive processes in a computer supported collaborative learning environment

The current understanding of students’ group metacognition is limited. The research on metacognition has focused mainly on the individual student. The aim of this study was to address the void by developing a conceptual model to inform the use of scaffolds to facilitate group metacognition during mathematical problem solving in computer supported collaborative learning (CSCL) environments. An initial conceptual framework based on the literature from metacognition, cooperative learning, cooperative group metacognition, and computer supported collaborative learning was used to inform the study. In order to achieve the study aim, a design research methodology incorporating two cycles was used. The first cycle focused on the within-group metacognition for sixteen groups of primary school students working together around the computer; the second cycle included between-group metacognition for six groups of primary school students working together on the Knowledge Forum® CSCL environment. The study found that providing groups with group metacognitive scaffolds resulted in groups planning, monitoring, and evaluating the task and team aspects of their group work. The metacognitive scaffolds allowed students to focus on how their group was completing the problem-solving task and working together as a team. From these findings, a revised conceptual model to inform the use of scaffolds to facilitate group metacognition during mathematical problem solving in computer supported collaborative learning (CSCL) environments was generated.

Sex offenders in denial : a study into a group of forensic psychologists' attitudes regarding the corresponding impact upon risk assessment calculations and parole eligibility

A considerable proportion of convicted sex offenders maintain a stance of innocence and thus do not engage in recommended treatment programs. As a result, such offenders are often deemed to have outstanding criminogenic needs which may negatively impact upon risk assessment procedures and parole eligibility. This paper reports on a study that aimed to investigate a group of forensic psychologists’ attitudes regarding the impact of denial on risk assessment ratings as well as parole eligibility. Participants completed a confidential open-ended questionnaire. Analysis indicated that considerable variability exists among forensic psychologists in regards to their beliefs about the origins of denial and what impact such denial should have on post-prison release eligibility. In contrast, there was less disparity regarding beliefs about the percentage of innocent yet incarcerated sex offenders. This paper also reviews current understanding regarding the impact of denial on recidivism as well as upon general forensic assessments.

Thai word segmentation with hidden Markov Model and decision tree

The Thai written language is one of the languages that does not have word boundaries. In order to discover the meaning of the document, all texts must be separated into syllables, words, sentences, and paragraphs. This paper develops a novel method to segment the Thai text by combining a non-dictionary based technique with a dictionary-based technique. This method first applies the Thai language grammar rules to the text for identifying syllables. The hidden Markov model is then used for merging possible syllables into words. The identified words are verified with a lexical dictionary and a decision tree is employed to discover the words unidentified by the lexical dictionary. Documents used in the litigation process of Thai court proceedings have been used in experiments. The results which are segmented words, obtained by the proposed method outperform the results obtained by other existing methods.