BMI not WHR modulates BOLD fMRI responses in a sub-cortical reward network when participants judge the attractiveness of human female bodies

In perceptual terms, the human body is a complex 3d shape which has to be interpreted by the observer to judge its attractiveness. Both body mass and shape have been suggested as strong predictors of female attractiveness. Normally body mass and shape co-vary, and it is difficult to differentiate their separate effects. A recent study suggested that altering body mass does not modulate activity in the reward mechanisms of the brain, but shape does. However, using computer generated female body-shaped greyscale images, based on a Principal Component Analysis of female bodies, we were able to construct images which covary with real female body mass (indexed with BMI) and not with body shape (indexed with WHR), and vice versa. Twelve observers (6 male and 6 female) rated these images for attractiveness during an fMRI study. The attractiveness ratings were correlated with changes in BMI and not WHR. Our primary fMRI results demonstrated that in addition to activation in higher visual areas (such as the extrastriate body area), changing BMI also modulated activity in the caudate nucleus, and other parts of the brain reward system. This shows that BMI, not WHR, modulates reward mechanisms in the brain and we infer that this may have important implications for judgements of ideal body size in eating disordered individuals.

Practical network coding for two way relay channels in LTE networks

In this paper, the implementation aspects and constraints of the simplest network coding (NC) schemes for a two-way relay channel (TWRC) composed of a user equipment (mobile terminal), an LTE relay station (RS) and an LTE base station (eNB) are considered in order to assess the usefulness of the NC in more realistic scenarios. The information exchange rate gain (IERG), the energy reduction gain (ERG) and the resource utilization gain (RUG) of the NC schemes with and without subcarrier division duplexing (SDD) are obtained by computer simulations. The usefulness of the NC schemes are evaluated for varying traffic load levels, the geographical distances between the nodes, the RS transmit powers, and the maximum numbers of retransmissions. Simulation results show that the NC schemes with and without SDD, have the throughput gains 0.5% and 25%, the ERGs 7 - 12% and 16 - 25%, and the RUGs 0.5 - 3.2%, respectively. It is found that the NC can provide performance gains also for the users at the cell edge. Furthermore, the ERGs of the NC increase with the transmit power of the relay while the ERGs of the NC remain the same even when the maximum number of retransmissions is reduced.

Learning task specific distributed paragraph representations using a 2-tier convolutional neural network

We introduce a type of 2-tier convolutional neural network model for learning distributed paragraph representations for a special task (e.g. paragraph or short document level sentiment analysis and text topic categorization). We decompose the paragraph semantics into 3 cascaded constitutes: word representation, sentence composition and document composition. Specifically, we learn distributed word representations by a continuous bag-of-words model from a large unstructured text corpus. Then, using these word representations as pre-trained vectors, distributed task specific sentence representations are learned from a sentence level corpus with task-specific labels by the first tier of our model. Using these sentence representations as distributed paragraph representation vectors, distributed paragraph representations are learned from a paragraph-level corpus by the second tier of our model. It is evaluated on DBpedia ontology classification dataset and Amazon review dataset. Empirical results show the effectiveness of our proposed learning model for generating distributed paragraph representations.

QoS guarantee with adaptive transmit power and message rate control for DSRC vehicle network based road safety applications

Quality of services (QoS) support is critical for dedicated short range communications (DSRC) vehicle networks based collaborative road safety applications. In this paper we propose an adaptive power and message rate control method for DSRC vehicle networks at road intersections. The design objective is to provide high availability and low latency channels for high priority emergency safety applications while maximizing channel utilization for low priority routine safety applications. In this method an offline simulation based approach is used to find out the best possible configurations of transmit power and message rate for given numbers of vehicles in the network. The identified best configurations are then used online by roadside access points (AP) according to estimated number of vehicles. Simulation results show that this adaptive method significantly outperforms a fixed control method. © 2011 Springer-Verlag.

Reinforcing the security of corporate information resources:a critical review of the role of the acceptable use policy

Increasingly users are seen as the weak link in the chain, when it comes to the security of corporate information. Should the users of computer systems act in any inappropriate or insecure manner, then they may put their employers in danger of financial losses, information degradation or litigation, and themselves in danger of dismissal or prosecution. This is a particularly important concern for knowledge-intensive organisations, such as universities, as the effective conduct of their core teaching and research activities is becoming ever more reliant on the availability, integrity and accuracy of computer-based information resources. One increasingly important mechanism for reducing the occurrence of inappropriate behaviours, and in so doing, protecting corporate information, is through the formulation and application of a formal ‘acceptable use policy (AUP). Whilst the AUP has attracted some academic interest, it has tended to be prescriptive and overly focussed on the role of the Internet, and there is relatively little empirical material that explicitly addresses the purpose, positioning or content of real acceptable use policies. The broad aim of the study, reported in this paper, is to fill this gap in the literature by critically examining the structure and composition of a sample of authentic policies – taken from the higher education sector – rather than simply making general prescriptions about what they ought to contain. There are two important conclusions to be drawn from this study: (1) the primary role of the AUP appears to be as a mechanism for dealing with unacceptable behaviour, rather than proactively promoting desirable and effective security behaviours, and (2) the wide variation found in the coverage and positioning of the reviewed policies is unlikely to be fostering a coherent approach to security management, across the higher education sector.

The information security policy unpacked:a critical study of the content of university policies

Ensuring the security of corporate information, that is increasingly stored, processed and disseminated using information and communications technologies [ICTs], has become an extremely complex and challenging activity. This is a particularly important concern for knowledge-intensive organisations, such as universities, as the effective conduct of their core teaching and research activities is becoming ever more reliant on the availability, integrity and accuracy of computer-based information resources. One increasingly important mechanism for reducing the occurrence of security breaches, and in so doing, protecting corporate information, is through the formulation and application of a formal information security policy (InSPy). Whilst a great deal has now been written about the importance and role of the information security policy, and approaches to its formulation and dissemination, there is relatively little empirical material that explicitly addresses the structure or content of security policies. The broad aim of the study, reported in this paper, is to fill this gap in the literature by critically examining the structure and content of authentic information security policies, rather than simply making general prescriptions about what they ought to contain. Having established the structure and key features of the reviewed policies, the paper critically explores the underlying conceptualisation of information security embedded in the policies. There are two important conclusions to be drawn from this study: (1) the wide diversity of disparate policies and standards in use is unlikely to foster a coherent approach to security management; and (2) the range of specific issues explicitly covered in university policies is surprisingly low, and reflects a highly techno-centric view of information security management.

Nootropia : a user profiling model based on a self-organising term network

Artificial Immune Systems are well suited to the problem of using a profile representation of an individual’s or a group’s interests to evaluate documents. Nootropia is a user profiling model that exhibits similarities to models of the immune system that have been developed in the context of autopoietic theory. It uses a self-organising term network that can represent a user’s multiple interests and can adapt to both short-term variations and substantial changes in them. This allows Nootropia to drift, constantly following changes in the user’s multiple interests, and, thus, to become structurally coupled to the user.

Neural network DEA for measuring the efficiency of mutual funds

Efficiency in the mutual fund (MF), is one of the issues that has attracted many investors in countries with advanced financial market for many years. Due to the need for frequent study of MF's efficiency in short-term periods, investors need a method that not only has high accuracy, but also high speed. Data envelopment analysis (DEA) is proven to be one of the most widely used methods in the measurement of the efficiency and productivity of decision making units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper uses neural network back-ropagation DEA in measurement of mutual funds efficiency and shows the requirements, in the proposed method, for computer memory and CPU time are far less than that needed by conventional DEA methods and can therefore be a useful tool in measuring the efficiency of a large set of MFs. Copyright © 2014 Inderscience Enterprises Ltd.

CamSim:a distributed smart camera network simulator

Smart cameras allow pre-processing of video data on the camera instead of sending it to a remote server for further analysis. Having a network of smart cameras allows various vision tasks to be processed in a distributed fashion. While cameras may have different tasks, we concentrate on distributed tracking in smart camera networks. This application introduces various highly interesting problems. Firstly, how can conflicting goals be satisfied such as cameras in the network try to track objects while also trying to keep communication overhead low? Secondly, how can cameras in the network self adapt in response to the behavior of objects and changes in scenarios, to ensure continued efficient performance? Thirdly, how can cameras organise themselves to improve the overall network's performance and efficiency? This paper presents a simulation environment, called CamSim, allowing distributed self-adaptation and self-organisation algorithms to be tested, without setting up a physical smart camera network. The simulation tool is written in Java and hence allows high portability between different operating systems. Relaxing various problems of computer vision and network communication enables a focus on implementing and testing new self-adaptation and self-organisation algorithms for cameras to use.

Evaluation efficiency of large-scale data set with negative data:an artificial neural network approach

Data envelopment analysis (DEA) is the most widely used methods for measuring the efficiency and productivity of decision-making units (DMUs). The need for huge computer resources in terms of memory and CPU time in DEA is inevitable for a large-scale data set, especially with negative measures. In recent years, wide ranges of studies have been conducted in the area of artificial neural network and DEA combined methods. In this study, a supervised feed-forward neural network is proposed to evaluate the efficiency and productivity of large-scale data sets with negative values in contrast to the corresponding DEA method. Results indicate that the proposed network has some computational advantages over the corresponding DEA models; therefore, it can be considered as a useful tool for measuring the efficiency of DMUs with (large-scale) negative data.

Driving licensing renewal policy using neural network-based probabilistic decision support system

This paper investigates neural network-based probabilistic decision support system to assess drivers' knowledge for the objective of developing a renewal policy of driving licences. The probabilistic model correlates drivers' demographic data to their results in a simulated written driving exam (SWDE). The probabilistic decision support system classifies drivers' into two groups of passing and failing a SWDE. Knowledge assessment of drivers within a probabilistic framework allows quantifying and incorporating uncertainty information into the decision-making system. The results obtained in a Jordanian case study indicate that the performance of the probabilistic decision support systems is more reliable than conventional deterministic decision support systems. Implications of the proposed probabilistic decision support systems on the renewing of the driving licences decision and the possibility of including extra assessment methods are discussed.

Impact of denial of service solutions on network quality of service

The Internet has become a universal communication network tool. It has evolved from a platform that supports best-effort traffic to one that now carries different traffic types including those involving continuous media with quality of service (QoS) requirements. As more services are delivered over the Internet, we face increasing risk to their availability given that malicious attacks on those Internet services continue to increase. Several networks have witnessed denial of service (DoS) and distributed denial of service (DDoS) attacks over the past few years which have disrupted QoS of network services, thereby violating the Service Level Agreement (SLA) between the client and the Internet Service Provider (ISP). Hence DoS or DDoS attacks are major threats to network QoS. In this paper we survey techniques and solutions that have been deployed to thwart DoS and DDoS attacks and we evaluate them in terms of their impact on network QoS for Internet services. We also present vulnerabilities that can be exploited for QoS protocols and also affect QoS if exploited. In addition, we also highlight challenges that still need to be addressed to achieve end-to-end QoS with recently proposed DoS/DDoS solutions. © 2010 John Wiley & Sons, Ltd.