Scalable black-box prediction models for multi-dimensional adaptation on NUMA multi-cores

This paper presents a scalable, statistical ‘black-box’ model for predicting the performance of parallel programs on multi-core non-uniform memory access (NUMA) systems. We derive a model with low overhead, by reducing data collection and model training time. The model can accurately predict the behaviour of parallel applications in response to changes in their concurrency, thread layout on NUMA nodes, and core voltage and frequency. We present a framework that applies the model to achieve significant energy and energy-delay-square (ED2) savings (9% and 25%, respectively) along with performance improvement (10% mean) on an actual 16-core NUMA system running realistic application workloads. Our prediction model proves substantially more accurate than previous efforts.

Sentinel lymph node biopsy in breast cancer - is lymphoscintigraphy really necessary?

The role of lymphoscintigraphy in sentinel node biopsy in breast cancer remains debatable. This study assesses the value of lymphoscintigraphy in axillary sentinel node biopsy in women undergoing surgery for breast cancer. Sixty-two patients underwent sentinel node biopsy using a combination of technetium-label led nanocolloid, lymphoscintigraphy and patent blue dye. Lymphoscintigraphy was successful in 84% of patients. Axillary sentinel nodes were identified intraoperatively in all these patients. Internal mammary nodes were identified on lymphoscintigraphy in 19%. Despite lymphoscintigraphy being unsuccessful in 10 patients, axillary sentinel nodes were found intraoperatively in eight of these patients. Lymphoscintigraphy did not increase the detection rate of axillary sentinel nodes and a negative scan did not preclude identification of an axillary sentinel node intraoperatively. This study questions the contribution of lymphoscintigraphy in axillary sentinel node biopsy, however its value may lie in the detection of extra-axillary nodes. (C) 2002 Published by Elsevier Science Ltd.

The Map of Watchful Architecture

The international border between Northern Ireland and the South (the Republic of Ireland) came into being in 1922, but there are many important historical antecedents to the notion and expression of spatial separation in this borderland. The author identifies historical precedents and links them to recent defensive features in order to generate a 'map of watchful architecture' that includes recent and much older elements.

On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables

Influence diagrams are intuitive and concise representations of structured decision problems. When the problem is non-Markovian, an optimal strategy can be exponentially large in the size of the diagram. We can avoid the inherent intractability by constraining the size of admissible strategies, giving rise to limited memory influence diagrams. A valuable question is then how small do strategies need to be to enable efficient optimal planning. Arguably, the smallest strategies one can conceive simply prescribe an action for each time step, without considering past decisions or observations. Previous work has shown that finding such optimal strategies even for polytree-shaped diagrams with ternary variables and a single value node is NP-hard, but the case of binary variables was left open. In this paper we address such a case, by first noting that optimal strategies can be obtained in polynomial time for polytree-shaped diagrams with binary variables and a single value node. We then show that the same problem is NP-hard if the diagram has multiple value nodes. These two results close the fixed-parameter complexity analysis of optimal strategy selection in influence diagrams parametrized by the shape of the diagram, the number of value nodes and the maximum variable cardinality.

The Impact of the Recent Economic Recession on the NEC Contract in Northern Ireland: A Preliminary Study

In comparison to the rest of the United Kingdom (UK) the Northern Irish Construction Industry was disproportionately affected by the recent economic recession. During this period, use of the New Engineering Contract (NEC) has proliferated in the public sector, however no study has been undertaken to examine the impact of the recession on this contract in Northern Ireland. The aim of this paper is to explore NEC contract implementation in Northern Ireland and the impact of the recession on its operation. A qualitative methodology is adopted using a literature review and semi structured interviews with six construction professionals. A qualitative analysis identifies themes and issues arising exploring connections and links between them using thematic coding. The initial findings are that the introduction of the NEC contract in Northern Ireland makes demands of contractors and consultants in terms of additional resources and training. Some consultants show a clear lack of understanding of the contract and its provisions. Whilst there is general agreement that the contract does help to stimulate good project management, the interviewees find the contract time consuming and complicated to administer, describing it as “unforgiving for the architect and unforgiving to the contractor.” Due to the impact of the recession, both contractors and consultants are still reporting a drop in income from pre-recession levels. Project resourcing levels have dropped significantly since the onset of the recession. Adversarial and opportunistic behaviour has increased. Many consultants and contractors are struggling to adequately administer the NEC contract at current income levels. The introduction of the NEC contract and the economic recession have exerted opposing forces on the implementation of the contract, hindering its execution. As the pressures exerted by the economic recession abate and a greater understanding of the contract develops, these opposing forces will ease leading to a more consistent implementation of the contract.

A Lightweight Tool for Anomaly Detection in Cloud Data Centres

Cloud data centres are critical business infrastructures and the fastest growing service providers. Detecting anomalies in Cloud data centre operation is vital. Given the vast complexity of the data centre system software stack, applications and workloads, anomaly detection is a challenging endeavour. Current tools for detecting anomalies often use machine learning techniques, application instance behaviours or system metrics distribu- tion, which are complex to implement in Cloud computing environments as they require training, access to application-level data and complex processing. This paper presents LADT, a lightweight anomaly detection tool for Cloud data centres that uses rigorous correlation of system metrics, implemented by an efficient corre- lation algorithm without need for training or complex infrastructure set up. LADT is based on the hypothesis that, in an anomaly-free system, metrics from data centre host nodes and virtual machines (VMs) are strongly correlated. An anomaly is detected whenever correlation drops below a threshold value. We demonstrate and evaluate LADT using a Cloud environment, where it shows that the hosting node I/O operations per second (IOPS) are strongly correlated with the aggregated virtual machine IOPS, but this correlation vanishes when an application stresses the disk, indicating a node-level anomaly.

Inactivating mutations in an SH2 domain-encoding gene in X-linked lymphoproliferative syndrome

X-linked lymphoproliferative syndrome (XLP) is an inherited immunodeficiency characterized by increased susceptibility to Epstein-Barr virus (EBV). In affected males, primary EBV infection leads to the uncontrolled proliferation of virus-containing B cells and reactive cytotoxic T cells, often culminating in the development of high-grade lymphoma. The XLP gene has been mapped to chromosome band Xq25 through linkage analysis and the discovery of patients harboring large constitutional genomic deletions. We describe here the presence of small deletions and intragenic mutations that specifically disrupt a gene named DSHP in 6 of 10 unrelated patients with XLP. This gene encodes a predicted protein of 128 amino acids composing a single SH2 domain with extensive homology to the SH2 domain of SHIP, an inositol polyphosphate 5-phosphatase that functions as a negative regulator of lymphocyte activation. DSHP is expressed in transformed T cell lines and is induced following in vitro activation of peripheral blood T lymphocytes. Expression of DSHP is restricted in vivo to lymphoid tissues, and RNA in situ hybridization demonstrates DSHP expression in activated T and B cell regions of reactive lymph nodes and in both T and B cell neoplasms. These observations confirm the identity of DSHP as the gene responsible for XLP, and suggest a role in the regulation of lymphocyte activation and proliferation. Induction of DSHP may sustain the immune response by interfering with SHIP-mediated inhibition of lymphocyte activation, while its inactivation in XLP patients results in a selective immunodeficiency to EBV.

Fast Byzantine Leader Election in Dynamic Networks

We study the fundamental Byzantine leader election problem in dynamic networks where the topology can change from round to round and nodes can also experience heavy {\em churn} (i.e., nodes can join and leave the network continuously over time). We assume the full information model where the Byzantine nodes have complete knowledge about the entire state of the network at every round (including random choices made by all the nodes), have unbounded computational power and can deviate arbitrarily from the protocol. The churn is controlled by an adversary that has complete knowledge and control over which nodes join and leave and at what times and also may rewire the topology in every round and has unlimited computational power, but is oblivious to the random choices made by the algorithm. Our main contribution is an $O(\log^3 n)$ round algorithm that achieves Byzantine leader election under the presence of up to $O({n}^{1/2 - \epsilon})$ Byzantine nodes (for a small constant $\epsilon > 0$) and a churn of up to \\$O(\sqrt{n}/\poly\log(n))$ nodes per round (where $n$ is the stable network size).The algorithm elects a leader with probability at least $1-n^{-\Omega(1)}$ and guarantees that it is an honest node with probability at least $1-n^{-\Omega(1)}$; assuming the algorithm succeeds, the leader's identity will be known to a $1-o(1)$ fraction of the honest nodes. Our algorithm is fully-distributed, lightweight, and is simple to implement. It is also scalable, as it runs in polylogarithmic (in $n$) time and requires nodes to send and receive messages of only polylogarithmic size per round.To the best of our knowledge, our algorithm is the first scalable solution for Byzantine leader election in a dynamic network with a high rate of churn; our protocol can also be used to solve Byzantine agreement in a straightforward way.We also show how to implement an (almost-everywhere) public coin with constant bias in a dynamic network with Byzantine nodes and provide a mechanism for enabling honest nodes to store information reliably in the network, which might be of independent interest.

The use of eye-tracking to explore social difficulties in cognitively able students with autism spectrum disorder: A pilot study

Individuals with autism spectrum disorder do not just 'grow out of' their early difficulties in understanding the social world. Even for those who are cognitively able, autism-related difficulties continue into adulthood. Atypicalities attending to and interpreting communicative signals from others can provide barriers to success in education, employment and relationships. In the current study, we use eye-tracking during real social interaction to explore attention to social cues (e.g. face, eyes, mouth) and links to social awareness in a group of cognitively able University students with autism spectrum disorder and typically developing students from the same University. During the interaction, students with autism spectrum disorder showed less eye fixation and more mouth fixation than typically developing students. Importantly, while 63% of typically developing participants reported thinking they were deceived about the true nature of the interaction, only 9% of autism spectrum disorder participants picked up this subtle social signal. We argue that understanding how these social attentional and social awareness difficulties manifest during adulthood is important given the growing number of adults with autism spectrum disorder who are attending higher level education. These adults may be particularly susceptible to drop-out due to demands of coping in situations where social awareness is so important.

Recognising difference while promoting cohesion: the role of collaborative networks in education

The chapter outlines various approaches taken to the challenge of divided societies through education systems and links this to debates on political approaches to conflict resolution in Northern Ireland. The education debate has structural and affective components, while the political debate revolves around the privileging of identity or commonality. Applying the principle of recognition and the importance of tolerance is seen to provide a range of policy options, but the evidence of education interventions in Northern Ireland does not highlight any one policy option as the most effective, nor did the end of the violent conflict produce a new consensus on a way forward. The chapter argues that a key additional element to effective practice might be provided through the promotion of shared education through collaborative networks of schools which allows for the continued operation of separate schools while promoting an integrated experience for pupils and teachers.

Simultaneous Measurements of the Channel Response for Multiple Eavesdroppers Operating in the Vicinity of a Body Area Network at 2.45 GHz

Channel randomness can be exploited to generate secret keys. However, to ensure secrecy, it is necessary that the channel response of any eavesdropping party remain sufficiently de-correlated with that of the legitimate users'. In this paper, we investigate whether such de-correlation occurs for a body area network (BAN) operating in an indoor environment at 2.45 GHz. The hypothetical BAN configuration consisted of two legitimate transceivers, one situated on the user's left wrist and the other on the user's waist. The eavesdroppers were positioned in either a co-located or distributed manner in the area surrounding the BAN user. Using the simultaneous channel response measured at the legitimate BAN nodes and the eavesdropper positions for stationary and mobile scenarios, we analyze the localized correlation coefficient. This allows us to determine if it is possible to generate secret keys in the presence of multiple eavesdroppers in an indoor environment. Our experimental results show that although channel reciprocity was observed for both the stationary and the mobile scenarios, a higher de-correlation between the legitimate users' channels was observed for the stationary case. This indicates that mobile scenarios are better suited for secret key generation.

Internet of Things. A proposed secured network topology

The myriad of technologies and protocols working at different layers pose significant security challenges in the upcoming Internet of Things (IoT) paradigm. Security features and needs vary from application to application and it is layer specific. In addition, security has to consider the constraints imposed by energy limited sensor nodes and consider the specific target application in order to provide security at different layers. This paper analyses current standardization efforts and protocols. It proposes a generic secured network topology for IoT and describes the relevant security challenges. Some exploitation examples are also provided.

Distributed Agent-Based Load Balancer for Cloud Computing

In this paper we present a concept of an agent-based strategy to allocate services on a Cloud system without overloading nodes and maintaining the system stability with minimum cost. To provide a base for our research we specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. We also present an early version of simulation environment and a prototype of agent-based load balancer implemented in functional language Scala and Akka framework.

A Meta-Heuristic Load Balancer for Cloud Computing Systems

This paper introduces a strategy to allocate services on a cloud system without overloading the nodes and maintaining the system stability with minimum cost. We specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. A prototype meta-heuristic load balancer is demonstrated and experimental results are presented and discussed. We also propose a novel genetic algorithm, where population is seeded with the outputs of other meta-heuristic algorithms.

e-Recruitment: descrição e inferência sobre oferta e procura

Trabalho de Projeto para obtenção do grau de Mestre em Engenharia Informática e de Computadores