Australian Governments and dilemmas in filtering the Internet: juggling freedoms against potential for harm

This paper examines proposed internet filtering policies in Australia from the 1990s to 2014 and discusses some of their ideological underpinnings. Executive summary The Internet is a revolutionary source of information and its dissemination; and a medium for collaboration and interaction between individuals without regard for geographic location. Since its inception, however, concerns have been raised about the potential for unsavoury characters to use the Internet as a vehicle for distributing pornography and material of a violent nature to young or otherwise vulnerable individuals. Governments across the world have attempted to deal with such activities by various means and to varying degrees. These have included imposing mandatory filtering at an Internet Service Provider (ISP) level and optional filtering at the computer level. In Australia there has been considerable debate about what degree of filtering (if any) should be mandated. The Howard Government favoured an approach which emphasised self-regulation by ISPs combined with a legislative component and education and freedom for families to choose between either computer or ISP filtering based on a list of unacceptable content. The Rudd and Gillard Governments preferred the option of a mandatory ISP level filter, although this too was to be based on a ‘blacklist’ of prohibited content. Both options have been criticised as being expensive and inefficient. In addition, it has been argued that the Rudd/Gillard option would have had a detrimental impact on Internet speeds and that it would set a precedent for future governments to widen filtering to other forms of expression. The Howard Government’s programs were largely discarded by Labor after it was elected in 2007. However, Labor’s own filtering option was abandoned prior to its defeat in the 2013 election. In conjunction with their filtering options , both Coalition and Labor Governments have supported education and information campaigns to assist people, particularly children, to deal with online predators and both have introduced successful programs. The current Coalition Government’s policy on Internet filtering appears to favour light-handed legislation combined with education and information programs. This paper examines the iterations of internet filtering policies from the 1990s to 2014 and discusses some of their ideological underpinnings

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

Learning Tree-based Deep Model for Recommender Systems

Model-based methods for recommender systems have been studied extensively in recent years. In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full corpus retrieval extremely difficult. To overcome the calculation barriers, models such as matrix factorization resort to inner product form (i.e., model user-item preference as the inner product of user, item latent factors) and indexes to facilitate efficient approximate k-nearest neighbor searches. However, it still remains challenging to incorporate more expressive interaction forms between user and item features, e.g., interactions through deep neural networks, because of the calculation cost. In this paper, we focus on the problem of introducing arbitrary advanced models to recommender systems with large corpus. We propose a novel tree-based method which can provide logarithmic complexity w.r.t. corpus size even with more expressive models such as deep neural networks. Our main idea is to predict user interests from coarse to fine by traversing tree nodes in a top-down fashion and making decisions for each user-node pair. We also show that the tree structure can be jointly learnt towards better compatibility with users' interest distribution and hence facilitate both training and prediction. Experimental evaluations with two large-scale real-world datasets show that the proposed method significantly outperforms traditional methods. Online A/B test results in Taobao display advertising platform also demonstrate the effectiveness of the proposed method in production environments.Comment: Accepted by KDD 201

Context Trees: Augmenting Geospatial Trajectories with Context

Exposing latent knowledge in geospatial trajectories has the potential to provide a better understanding of the movements of individuals and groups. Motivated by such a desire, this work presents the context tree, a new hierarchical data structure that summarises the context behind user actions in a single model. We propose a method for context tree construction that augments geospatial trajectories with land usage data to identify such contexts. Through evaluation of the construction method and analysis of the properties of generated context trees, we demonstrate the foundation for understanding and modelling behaviour afforded. Summarising user contexts into a single data structure gives easy access to information that would otherwise remain latent, providing the basis for better understanding and predicting the actions and behaviours of individuals and groups. Finally, we also present a method for pruning context trees, for use in applications where it is desirable to reduce the size of the tree while retaining useful information

Empowering and assisting natural human mobility: The simbiosis walker

This paper presents the complete development of the Simbiosis Smart Walker. The device is equipped with a set of sensor subsystems to acquire user-machine interaction forces and the temporal evolution of user's feet during gait. The authors present an adaptive filtering technique used for the identification and separation of different components found on the human-machine interaction forces. This technique allowed isolating the components related with the navigational commands and developing a Fuzzy logic controller to guide the device. The Smart Walker was clinically validated at the Spinal Cord Injury Hospital of Toledo - Spain, presenting great acceptability by spinal chord injury patients and clinical staf

Pulling all the pedagogic pieces together to provide good electronic feedback: an example from science education

The aim of Nefreduca project was to develop digital science materials for chronically ill children and to assist them to cope more rationally with their illness. The Nefreduca system was developed in 2 stages.The first was a teaching-learning sequence about nutrition and kidney function, which was tested with the students and their teachers from the hospital school. The second phase was the construction of an automatic feedback system for Nefreduca. This paper reports the findings from both phases and makes the case that good feedback models must build upon on a good learning design that addresses students' misconceptions and introduces well structured e-activitie

Personalized Fuzzy Text Search Using Interest Prediction and Word Vectorization

In this paper we study the personalized text search problem. The keyword based search method in conventional algorithms has a low efficiency in understanding users' intention since the semantic meaning, user profile, user interests are not always considered. Firstly, we propose a novel text search algorithm using a inverse filtering mechanism that is very efficient for label based item search. Secondly, we adopt the Bayesian network to implement the user interest prediction for an improved personalized search. According to user input, it searches the related items using keyword information, predicted user interest. Thirdly, the word vectorization is used to discover potential targets according to the semantic meaning. Experimental results show that the proposed search engine has an improved efficiency and accuracy and it can operate on embedded devices with very limited computational resources

An agent-based DDM for high level architecture

The Data Distribution Management (DDM) service is one of the six services provided in the Runtime Infrastructure (RTI) of High Level Architecture (HLA). Its purpose is to perform data filtering and reduce irrelevant data communicated between federates. The two DDM schemes proposed for RTI, region based and grid based DDM, are oriented to send as little irrelevant data to subscribers as possible, but only manage to filter part of this information and some irrelevant data is still being communicated. Previously (G. Tan et al., 2000), we employed intelligent agents to perform data filtering in HLA, implemented an agent based DDM in RTI (ARTI) and compared it with the other two filtering mechanisms. The paper reports on additional experiments, results and analysis using two scenarios: the AWACS sensing aircraft simulation and the air traffic control simulation scenario. Experimental results show that compared with other mechanisms, the agent based approach communicates only relevant data and minimizes network communication, and is also comparable in terms of time efficiency. Some guidelines on when the agent based scheme can be used are also give

Reciprocal Recommendation System for Online Dating

Online dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos, etc) with a user's interests, a recommendation system for online dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the online dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential dating candidate is computed and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major online dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall. Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates. In particular, males tend to be focused on their own interest and oblivious towards their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line