Behavior-Based branch prediction by dynamically clustering branch instructions

Conditional branches frequently exhibit similar behavior (bias, time-varying behavior,...), a property that can be used to improve branch prediction accuracy. Branch clustering constructs groups or clusters of branches with similar behavior and applies different branch prediction techniques to each branch cluster. We revisit the topic of branch clustering with the aim of generalizing branch clustering. We investigate several methods to measure cluster information, with the most effective the storage of information in the branch target buffer. Also, we investigate alternative methods of using the branch cluster identification in the branch predictor. By these improvements we arrive at a branch clustering technique that obtains higher accuracy than previous approaches presented in the literature for the gshare predictor. Furthermore, we evaluate our branch clustering technique in a wide range of predictors to show the general applicability of the method. Branch clustering improves the accuracy of the local history (PAg) predictor, the path-based perceptron and the PPM-like predictor, one of the 2004 CBP finalists.

Tools for Finding Inconsistencies in Real-world Logic-based Systems

Currently there is extensive theoretical work on inconsistencies in logic-based systems. Recently, algorithms for identifying inconsistent clauses in a single conjunctive formula have demonstrated that practical application of this work is possible. However, these algorithms have not been extended for full knowledge base systems and have not been applied to real-world knowledge. To address these issues, we propose a new algorithm for finding the inconsistencies in a knowledge base using existing algorithms for finding inconsistent clauses in a formula. An implementation of this algorithm is then presented as an automated tool for finding inconsistencies in a knowledge base and measuring the inconsistency of formulae. Finally, we look at a case study of a network security rule set for exploit detection (QRadar) and suggest how these automated tools can be applied.

Intrusion Detection System for Network Security in Synchrophasor Systems

Synchrophasor systems will play a crucial role in next generation Smart Grid monitoring, protection and control. However these systems also introduce a multitude of potential vulnerabilities from malicious and inadvertent attacks, which may render erroneous operation or severe damage. This paper proposes a Synchrophasor Specific Intrusion Detection System (SSIDS) for malicious cyber attack and unintended misuse. The SSIDS comprises a heterogeneous whitelist and behavior-based approach to detect known attack types and unknown and so-called ‘zero-day’ vulnerabilities and attacks. The paper describes reconnaissance, Man-in-the-Middle (MITM) and Denial-of-Service (DoS) attack types executed against a practical synchrophasor system which are used to validate the real-time effectiveness of the proposed SSIDS cyber detection method.

Pre-charging and DC Fault Ride-Through of Hybri MMC Based HVDC Systems

Compared to half-bridge based MMCs, full-bridge based systems have the advantage of blocking dc fault, but at the expense of increased power semiconductors and power losses. In view of the relationships among ac/dc voltages and currents in full-bridge based MMC with the negative voltage state, this paper provides a detailed analysis on the link between capacitor voltage variation and the maximum modulation index. A hybrid MMC, consisting of mixed half-bridge and full-bridge circuits to combine their respective advantages is investigated in terms of its pre-charging process and transient dc fault ride-through capability. Simulation and experiment results demonstrate the feasibility and validity of the proposed strategy for a full-bridge based MMC and the hybrid MMC.

A review of the welfare of dairy cows in continuously housed and pasture-based production systems.

There is increasing interest in the use of continuous housing systems for dairy cows, with various reasons put forward to advocate such systems. However, the welfare of dairy cows is typically perceived to be better within pasture-based systems, although such judgements are often not scientifically based. The aim of this review was to interrogate the existing scientific literature to compare the welfare, including health, of dairy cows in continuously housed and pasture-based systems. While summarising existing work, knowledge gaps and directions for future research are also identified. The scope of the review is broad, examining relevant topics under three main headings; health, behaviour, and physiology. Regarding health, cows on pasture-based systems had lower levels of lameness, hoof pathologies, hock lesions, mastitis, uterine disease, and mortality compared to cows on continuously housed systems. Pasture access also had benefits for dairy cow behaviour, in terms of grazing, improved lying / resting times, and lower levels of aggression. Moreover, when given the choice between pasture and indoor housing, cows showed an overall preference for pasture, particularly at night. However, the review highlighted the need for a deeper understanding of cow preference and behaviour. Potential areas for concern within pasture-based systems included physiological indicators of more severe negative energy balance, and in some situations, the potential for compromised welfare with exposure to unpredictable weather conditions. In summary, the results from this review highlight that there remain considerable animal welfare benefits from incorporating pasture access into dairy production systems.

Fuzzy logic-based implicit authentication for mobile access control

In order to address the increasing compromise of user privacy on mobile devices, a Fuzzy Logic based implicit authentication scheme is proposed in this paper. The proposed scheme computes an aggregate score based on selected features and a threshold in real-time based on current and historic data depicting user routine. The tuned fuzzy system is then applied to the aggregated score and the threshold to determine the trust level of the current user. The proposed fuzzy-integrated implicit authentication scheme is designed to: operate adaptively and completely in the background, require minimal training period, enable high system accuracy while provide timely detection of abnormal activity. In this paper, we explore Fuzzy Logic based authentication in depth. Gaussian and triangle-based membership functions are investigated and compared using real data over several weeks from different Android phone users. The presented results show that our proposed Fuzzy Logic approach is a highly effective, and viable scheme for lightweight real-time implicit authentication on mobile devices.

An extended framework for evidential reasoning systems.

Use of the Dempster-Shafer (D-S) theory of evidence to deal with uncertainty in knowledge-based systems has been widely addressed. Several AI implementations have been undertaken based on the D-S theory of evidence or the extended theory. But the representation of uncertain relationships between evidence and hypothesis groups (heuristic knowledge) is still a major problem. This paper presents an approach to representing such knowledge, in which Yen’s probabilistic multi-set mappings have been extended to evidential mappings, and Shafer’s partition technique is used to get the mass function in a complex evidence space. Then, a new graphic method for describing the knowledge is introduced which is an extension of the graphic model by Lowrance et al. Finally, an extended framework for evidential reasoning systems is specified.

A single microphone capillary-based system for measuring the complex input impedance of musical wind instruments

Capillary-based systems for measuring the input impedance of musical wind instruments were first developed in the mid-20th century and remain in widespread use today. In this paper, the basic principles and assumptions underpinning the design of such systems are examined. Inexpensive modifications to a capillary-based impedance measurement set-up made possible due to advances in computing and data acquisition technology are discussed. The modified set-up is able to measure both impedance magnitude and impedance phase even though it only contains one microphone. In addition, a method of calibration is described that results in a significant improvement in accuracy when measuring high impedance objects on the modified capillary-based system. The method involves carrying out calibration measurements on two different objects whose impedances are well-known theoretically. The benefits of performing two calibration measurements (as opposed to the one calibration measurement that has been traditionally used) are demonstrated experimentally through input impedance measurements on two test objects and a Boosey and Hawkes oboe. © S. Hirzel Verlag · EAA.

Generic low-latency NoC router architecture for FPGA computing systems

A novel cost-effective and low-latency wormhole router for packet-switched NoC designs, tailored for FPGA, is presented. This has been designed to be scalable at system level to fully exploit the characteristics and constraints of FPGA based systems, rather than custom ASIC technology. A key feature is that it achieves a low packet propagation latency of only two cycles per hop including both router pipeline delay and link traversal delay - a significant enhancement over existing FPGA designs - whilst being very competitive in terms of performance and hardware complexity. It can also be configured in various network topologies including 1-D, 2-D, and 3-D. Detailed design-space exploration has been carried for a range of scaling parameters, with the results of various design trade-offs being presented and discussed. By taking advantage of abundant buildin reconfigurable logic and routing resources, we have been able to create a new scalable on-chip FPGA based router that exhibits high dimensionality and connectivity. The architecture proposed can be easily migrated across many FPGA families to provide flexible, robust and cost-effective NoC solutions suitable for the implementation of high-performance FPGA computing systems. © 2011 IEEE.

Multi-Attribute SCADA-Specific Intrusion Detection System for Power Networks

The increased interconnectivity and complexity of supervisory control and data acquisition (SCADA) systems in power system networks has exposed the systems to a multitude of potential vulnerabilities. In this paper, we present a novel approach for a next-generation SCADA-specific intrusion detection system (IDS). The proposed system analyzes multiple attributes in order to provide a comprehensive solution that is able to mitigate varied cyber-attack threats. The multiattribute IDS comprises a heterogeneous white list and behavior-based concept in order to make SCADA cybersystems more secure. This paper also proposes a multilayer cyber-security framework based on IDS for protecting SCADA cybersecurity in smart grids without compromising the availability of normal data. In addition, this paper presents a SCADA-specific cybersecurity testbed to investigate simulated attacks, which has been used in this paper to validate the proposed approach.

Managing adaptivity in parallel systems

The management of non-functional features (performance, security, power management, etc.) is traditionally a difficult, error prone task for programmers of parallel applications. To take care of these non-functional features, autonomic managers running policies represented as rules using sensors and actuators to monitor and transform a running parallel application may be used. We discuss an approach aimed at providing formal tool support to the integration of independently developed autonomic managers taking care of different non-functional concerns within the same parallel application. Our approach builds on the Behavioural Skeleton experience (autonomic management of non-functional features in structured parallel applications) and on previous results on conflict detection and resolution in rule-based systems. © 2013 Springer-Verlag Berlin Heidelberg.

What Went Wrong With the Mate-Tricks After-School Program, Which Increased Antisocial Behavior and Child/Parent Conflict?

This paper draws from an independent RCT evaluation on a behavior based afterschool intervention for called Mate-Tricks for 9-10 year old children and their families (N=592). This paper explores practical and theoretical issues that may have contributed to a range of iatrogenic effects found by the evaluation. To do this the paper focuses on key practical implementation factors such as: program exposure; engagement; and program quality. The paper also relates these results to popular theories of social development, including social interdependence theory. Finally, the paper discusses what the results suggest about the impact of cooperative/competitive goal structures in child and parent interventions of this type.

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.