Crime, justice and the legitimacy of military power in the international sphere

This article examines how a discourse of crime and justice is beginning to play a significant role in justifying international military operations. It suggests that although the coupling of war with crime and justice is not a new phenomenon, its present manifestations invite careful consideration of the connection between crime and political theory. It starts by reviewing the notion of sovereignty to look then at the history of the criminalisation of war and the emergence of new norms to constrain sovereign states. In this context, it examines the three ways in which military force has recently been authorised: in Iraq, in Libya and through drones in Yemen, Pakistan and Somalia. It argues the contemporary coupling of military technology with notions of crime and justice allows the reiteration of the perpetration of crimes by the powerful and the representation of violence as pertaining to specific dangerous populations in the space of the international. It further suggests that this authorises new architectures of authority, fundamentally based on military power as a source of social power.

Adaptive Measurement-Based Policy-Driven QoS Management with Fuzzy-Rule-based Resource Allocation

Fixed and wireless networks are increasingly converging towards common connectivity with IP-based core networks. Providing effective end-to-end resource and QoS management in such complex heterogeneous converged network scenarios requires unified, adaptive and scalable solutions to integrate and co-ordinate diverse QoS mechanisms of different access technologies with IP-based QoS. Policy-Based Network Management (PBNM) is one approach that could be employed to address this challenge. Hence, a policy-based framework for end-to-end QoS management in converged networks, CNQF (Converged Networks QoS Management Framework) has been proposed within our project. In this paper, the CNQF architecture, a Java implementation of its prototype and experimental validation of key elements are discussed. We then present a fuzzy-based CNQF resource management approach and study the performance of our implementation with real traffic flows on an experimental testbed. The results demonstrate the efficacy of our resource-adaptive approach for practical PBNM systems

Parental leave regulations and the persistence of the male breadwinner model: Using fuzzy-set ideal type analysis to assess gender equality in an enlarged Europe

his paper uses fuzzy-set ideal type analysis to assess the conformity of European leave regulations to four theoretical ideal typical divisions of labour: male breadwinner, caregiver parity, universal breadwinner and universal caregiver. In contrast to the majority of previous studies, the focus of this analysis is on the extent to which leave regulations promote gender equality in the family and the transformation of traditional gender roles. The results of this analysis demonstrate that European countries cluster into five models that only partly coincide with countries’ geographical proximity. Second, none of the countries considered constitutes a universal caregiver model, while the male breadwinner ideal continues to provide the normative reference point for parental leave regulations in a large number of European states. Finally, we witness a growing emphasis at the national and EU levels concerning the universal breadwinner ideal, which leaves gender inequality in unpaid work unproblematized.

An Efficient LS-SVM-Based Method for Fuzzy System Construction

This paper proposes an efficient learning mechanism to build fuzzy rule-based systems through the construction of sparse least-squares support vector machines (LS-SVMs). In addition to the significantly reduced computational complexity in model training, the resultant LS-SVM-based fuzzy system is sparser while offers satisfactory generalization capability over unseen data. It is well known that the LS-SVMs have their computational advantage over conventional SVMs in the model training process; however, the model sparseness is lost, which is the main drawback of LS-SVMs. This is an open problem for the LS-SVMs. To tackle the nonsparseness issue, a new regression alternative to the Lagrangian solution for the LS-SVM is first presented. A novel efficient learning mechanism is then proposed in this paper to extract a sparse set of support vectors for generating fuzzy IF-THEN rules. This novel mechanism works in a stepwise subset selection manner, including a forward expansion phase and a backward exclusion phase in each selection step. The implementation of the algorithm is computationally very efficient due to the introduction of a few key techniques to avoid the matrix inverse operations to accelerate the training process. The computational efficiency is also confirmed by detailed computational complexity analysis. As a result, the proposed approach is not only able to achieve the sparseness of the resultant LS-SVM-based fuzzy systems but significantly reduces the amount of computational effort in model training as well. Three experimental examples are presented to demonstrate the effectiveness and efficiency of the proposed learning mechanism and the sparseness of the obtained LS-SVM-based fuzzy systems, in comparison with other SVM-based learning techniques.

Privacy-Protected Facial Biometric Verification via Fuzzy Forest Learning

Although visual surveillance has emerged as an effective technolody for public security, privacy has become an issue of great concern in the transmission and distribution of surveillance videos. For example, personal facial images should not be browsed without permission. To cope with this issue, face image scrambling has emerged as a simple solution for privacyrelated applications. Consequently, online facial biometric verification needs to be carried out in the scrambled domain thus bringing a new challenge to face classification. In this paper, we investigate face verification issues in the scrambled domain and propose a novel scheme to handle this challenge. In our proposed method, to make feature extraction from scrambled face images robust, a biased random subspace sampling scheme is applied to construct fuzzy decision trees from randomly selected features, and fuzzy forest decision using fuzzy memberships is then obtained from combining all fuzzy tree decisions. In our experiment, we first estimated the optimal parameters for the construction of the random forest, and then applied the optimized model to the benchmark tests using three publically available face datasets. The experimental results validated that our proposed scheme can robustly cope with the challenging tests in the scrambled domain, and achieved an improved accuracy over all tests, making our method a promising candidate for the emerging privacy-related facial biometric applications.

Towards Possibilistic Fuzzy Answer Set Programming

Fuzzy answer set programming (FASP) is a generalization of answer set programming to continuous domains. As it can not readily take uncertainty into account, however, FASP is not suitable as a basis for approximate reasoning and cannot easily be used to derive conclusions from imprecise information. To cope with this, we propose an extension of FASP based on possibility theory. The resulting framework allows us to reason about uncertain information in continuous domains, and thus also about information that is imprecise or vague. We propose a syntactic procedure, based on an immediate consequence operator, and provide a characterization in terms of minimal models, which allows us to straightforwardly implement our framework using existing FASP solvers.

High Performance Real-time Pre-Processing for Fixed-Complexity Sphere Decoder

Pre-processing (PP) of received symbol vector and channel matrices is an essential pre-requisite operation for Sphere Decoder (SD)-based detection of Multiple-Input Multiple-Output (MIMO) wireless systems. PP is a highly complex operation, but relative to the total SD workload it represents a relatively small fraction of the overall computational cost of detecting an OFDM MIMO frame in standards such as 802.11n. Despite this, real-time PP architectures are highly inefficient, dominating the resource cost of real-time SD architectures. This paper resolves this issue. By reorganising the ordering and QR decomposition sub operations of PP, we describe a Field Programmable Gate Array (FPGA)-based PP architecture for the Fixed Complexity Sphere Decoder (FSD) applied to 4 × 4 802.11n MIMO which reduces resource cost by 50% as compared to state-of-the-art solutions whilst maintaining real-time performance.

Six-year follow-up of the SPHERE RCT: secondary prevention of heart disease in general practice.

Objective: To determine the long-term effectiveness of a complex intervention in primary care aimed at improving outcomes for patients with coronary heart disease.

Design: A 6-year follow-up of a cluster randomised controlled trial, which found after 18 months that both total and cardiovascular hospital admissions were significantly reduced in intervention practices (8% absolute reduction).

Setting: 48 general practices in the Republic of Ireland and Northern Ireland.

Participants: 903 patients with established coronary heart disease at baseline in the original trial.

Intervention: The original intervention consisted of tailored practice and patient plans; training sessions for practitioners in medication prescribing and behavioural change; and regular patient recall system. Control practices provided usual care. Following the intervention period, all supports from the research team to intervention practices ceased.

Outcome measures: Primary outcome: hospital admissions, all cause and cardiovascular; secondary outcomes: mortality; blood pressure and cholesterol control.

Results: At 6-year follow-up, data were collected from practice records of 696 patients (77%). For those who had died, we censored their data at the point of death and cause of death was established. There were no significant differences between the intervention and control practices in either total (OR 0.83 (95% CI 0.54 to 1.28)) or cardiovascular hospital admissions (OR 0.91 (95% CI 0.49 to 1.65)). We confirmed mortality status of 886 of the original 903 patients (98%). There were no significant differences in mortality (15% in intervention and 16% in control) or in the proportions of patients above target control for systolic blood pressure or total cholesterol.

Conclusions: Initial significant differences in the numbers of total and cardiovascular hospital admissions were not maintained at 6 years and no differences were found in mortality or blood pressure and cholesterol control. Policymakers need to continue to assess the effectiveness of previously efficacious programmes.

Trial registration number: Current Controlled Trials ISRCTN24081411.

Philosophy and Political Engagement: Reflection in the Public Sphere

Do philosophers have a responsibility to their society that is distinct from their responsibility to it as citizens? This edited volume explores both what type of contribution philosophy can make and what type of reasoning is appropriate when addressing public matters now. These questions are posed by leading international scholars working in the fields of moral and political philosophy. Each contribution also investigates the central issue of how to combine critical, rational analysis with a commitment to politically relevant public engagement. The contributions to this volume analyse issues raised in practical ethics, including abortion, embryology, and assisted suicide. They consider the role of ethical commitment in the philosophical analysis of contemporary political issues, and engage with matters of public policy such as poverty, the arts, meaningful work, as well as the evidence base for policy. They also examine the normative legitimacy of power, including the use of violence.

Modelling of Evaporator in Waste Heat Recovery System using Finite Volume Method and Fuzzy Technique

The evaporator is an important component in the Organic Rankine Cycle (ORC)-based Waste Heat Recovery (WHR) system since the effective heat transfer of this device reflects on the efficiency of the system. When the WHR system operates under supercritical conditions, the heat transfer mechanism in the evaporator is unpredictable due to the change of thermo-physical properties of the fluid with temperature. Although the conventional finite volume model can successfully capture those changes in the evaporator of the WHR process, the computation time for this method is high. To reduce the computation time, this paper develops a new fuzzy based evaporator model and compares its performance with the finite volume method. The results show that the fuzzy technique can be applied to predict the output of the supercritical evaporator in the waste heat recovery system and can significantly reduce the required computation time. The proposed model, therefore, has the potential to be used in real time control applications.