A discriminative approach to grounded spoken language understanding in interactive robotics

Spoken Language Understanding in Interactive Robotics provides computational models of human-machine communication based on the vocal input. However, robots operate in specific environments and the correct interpretation of the spoken sentences depends on the physical, cognitive and linguistic aspects triggered by the operational environment. Grounded language processing should exploit both the physical constraints of the context as well as knowledge assumptions of the robot. These include the subjective perception of the environment that explicitly affects linguistic reasoning. In this work, a standard linguistic pipeline for semantic parsing is extended toward a form of perceptually informed natural language processing that combines discriminative learning and distributional semantics. Empirical results achieve up to a 40% of relative error reduction

Filling the gap : a learning network for health and human rights in the Western Cape, South Africa

We draw on the experience of a Learning Network for Health and Human Rights (LN) involving collaboration between academic institutions and civil society organizations in the Western Cape, South Africa, aimed at identifying and disseminating best practice related to the right to health. The LN’s work in materials development, participatory research, training and capacity-building for action, and advocacy for intervention illustrates important lessons for human rights practice. These include (i) the importance of active translation of knowledge and awareness into action for rights to be made real; (ii) the potential tension arising from civil society action, which might relieve the state of its obligations by delivering services that should be the state’s responsibility—and hence the importance of emphasizing civil society’s role in holding services accountable in terms of the right to health; (iii) the role of civil society organizations in filling a gap related to obligations to promote rights; (iv) the critical importance of networking and solidarity for building civil society capacity to act for health rights. Evidence from evaluation of the LN is presented to support the argument that civil society can play a key role in bridging a gap between formal state commitment to creating a human rights culture and realizing services and policies that enable the most vulnerable members of society to advance their health. Through access to information and the creation of spaces, both for participation and as a safe environment in which learning can be turned into practice, the agency of those most affected by rights violations can be redressed. We argue that civil society agency is critical to such action

Intrusion Detection System using Bayesian Network Modeling

Computer Network Security has become a critical and important issue due to ever increasing cyber-crimes. Cybercrimes are spanning from simple piracy crimes to information theft in international terrorism. Defence security agencies and other militarily related organizations are highly concerned about the confidentiality and access control of the stored data. Therefore, it is really important to investigate on Intrusion Detection System (IDS) to detect and prevent cybercrimes to protect these systems. This research proposes a novel distributed IDS to detect and prevent attacks such as denial service, probes, user to root and remote to user attacks. In this work, we propose an IDS based on Bayesian network classification modelling technique. Bayesian networks are popular for adaptive learning, modelling diversity network traffic data for meaningful classification details. The proposed model has an anomaly based IDS with an adaptive learning process. Therefore, Bayesian networks have been applied to build a robust and accurate IDS. The proposed IDS has been evaluated against the KDD DAPRA dataset which was designed for network IDS evaluation. The research methodology consists of four different Bayesian networks as classification models, where each of these classifier models are interconnected and communicated to predict on incoming network traffic data. Each designed Bayesian network model is capable of detecting a major category of attack such as denial of service (DoS). However, all four Bayesian networks work together to pass the information of the classification model to calibrate the IDS system. The proposed IDS shows the ability of detecting novel attacks by continuing learning with different datasets. The testing dataset constructed by sampling the original KDD dataset to contain balance number of attacks and normal connections. The experiments show that the proposed system is effective in detecting attacks in the test dataset and is highly accurate in detecting all major attacks recorded in DARPA dataset. The proposed IDS consists with a promising approach for anomaly based intrusion detection in distributed systems. Furthermore, the practical implementation of the proposed IDS system can be utilized to train and detect attacks in live network traffi