Miracle-dz/UniFAR-FTUs huggingface dataset:
Surveilling Strange Materialities: Categorisation in the Evolving Geographies of FMD Biosecurity

The 2001 foot and mouth disease (FMD) epidemic in the United Kingdom resulted in the popularisation of the concept of biosecurity. At its most basic, biosecurity refers to simple cleansing and disinfecting, but during the FMD epidemic it became associated with a powerful system of surveillance. We characterise surveillance as a thing in itself, a mode of ordering that can be added to others. The establishment and maintenance of categories are fundamental to the practice of surveillance, but social studies of surveillance have not yet fully realised the way such categorisation operates within the nonhuman and the spatial. Strange materialities are those things which do not quite belong within a particular order. We examine the actions of surveillance in the world of strange materialities that was the FMD epidemic. Here we see that surveillant practices acted on the nonhuman FMD virus by constructing territories to control humans. Surveillance seems to proceed as the translation of a worldview (a system of categorisation) into materiality and we conclude with some thoughts on what this may mean for geographical studies of technical, biological, and human materialities in which surveillant processes are at work.
Web data indexing in the cloud: efficiency and cost reductions

An increasing part of the world's data is either shared through the Web or directly produced through and for Web platforms, in particular using structured formats like XML or JSON. Cloud platforms are interesting candidates to handle large data repositories, due to their elastic scaling properties. Popular commercial clouds provide a variety of sub-systems and primitives for storing data in specific formats (files, key-value pairs etc.) as well as dedicated sub-systems for running and coordinating execution within the cloud. We propose an architecture for warehousing large-scale Web data, in particular XML, in a commercial cloud platform, specifically, Amazon Web Services. Since cloud users support monetary costs directly connected to their consumption of cloud resources, we focus on indexing content in the cloud. We study the applicability of several indexing strategies, and show that they lead not only to reducing query evaluation time, but also, importantly, to reducing the monetary costs associated with the exploitation of the cloud-based warehouse. Our architecture can be easily adapted to similar cloud-based complex data warehousing settings, carrying over the benefits of access path selection in the cloud.
State assignment of finite state machines using a genetic algorithm

The use of genetic algorithms for the generation of optimal state assignments for synchronous finite state machines (FSM) is proposed. Results are presented to show that, in all examples attempted, the resulting state assignments are better than or at least as good as those produced by SPECTRAL, NOVA and MUSTANG and also closed partition assignments. On average, the genetic algorithm produced assignments with 33% less logic than the best produced by other algorithms.
From Clinical Records to Regulatory Reporting: Formal Terminologies as Foundation

Achieving goals of healthy people and populations is dependent on available and relevant data for health care decisions. New technologies enable reuse of data for decisions, however it is clear that uniform data standards and in particular standards around terminological data will be required to achieve reuse. Terminological data related to functioning and disability presents unique challenges because of the conceptual ambiguity within the field. The International Classification of Functioning, Disability, and Health (ICF) provides a clarifying conceptual foundation for functioning and disability data, but is not structured as a formal terminology. The need for a concerted and coordinated effort is emphasized.
Single Nigrostriatal Dopaminergic Neurons Form Widely Spread and Highly Dense Axonal Arborizations in the Neostriatum

The axonal arbors of single nigrostriatal dopaminergic neurons were visualized with a viral vector expressing membrane-targeted green fluorescent protein in rat brain. All eight reconstructed tyrosine hydroxylase-positive dopaminergic neurons possessed widely spread and highly dense axonal arborizations in the neostriatum. All of them emitted very little axon collateral arborization outside of the striatum except for tiny arborization in the external pallidum. The striatal axonal bush of each reconstructed dopaminergic neuron covered 0.45–5.7% (mean ± SD = 2.7 ± 1.5%) of the total volume of the neostriatum. Furthermore, all the dopaminergic neurons innervated both striosome and matrix compartments of the neostriatum, although each neuron's arborization tended to favor one of these compartments. Our findings demonstrate that individual dopaminergic neurons of the substantia nigra can broadcast a dopamine signal and exert strong influence over a large number of striatal neurons. This divergent signaling should be a key to the function of the nigrostriatal system in dopamine-based learning and suggests that neurodegeneration of individual nigral neurons can affect multiple neurons in the striatum. Thus, these results would also contribute to understanding the clinicopathology of Parkinson's disease and related syndromes.
Joint gait-pose manifold for video-based human motion estimation

We propose a joint gait-pose manifold (JGPM) for human motion modeling that unifies two motion-related variables, i.e., pose (a specific posture in a walking cycle) and gait (an individual walking style), into one manifold representation. JGPM is able to capture the motion variability both across different poses and among multiple gaits simultaneously. We develop a new Gaussian processes (GP)-based dimension reduction algorithm to learn a torus-like JGPM that balances an ideal manifold structure and the intrinsic data structure. The learned JGPM outperforms existing GP-based methods in terms of the capability of gait interpolation. Also, JGPM is applied to video-based motion estimation in a particle filtering framework. Our algorithm is trained from the CMU Mocap data and tested on the Brown HumanEva dataset, and experimental results confirm the effectiveness of the proposed methods.
Modelling Affordances for the Control and Evaluation of Intrinsically Motivated Robots

In psychological theory, affordances provide a way to describe an environment in terms of the opportunities it provides an organism to act. Affordance-based models have been applied to robotics in areas such as tool-use, interaction and vision, as an alternative to hybrid control architectures. This paper introduces a model of affordances for controlling and evaluating intrinsically motivated robots. We present an implementation and experimental evaluation of the model on four critter-bots using the Lego Mindstorms NXT platform. Results show that the critter-bots can evolve measurable, structured behaviour cycles adapted to their individual physical structures.
A Machine-learning Approach for Classifying and Categorizing Android Sources and Sinks

Today’s smartphone users face a security dilemma: many apps they install operate on privacy-sensitive data, although they might originate from developers whose trustworthiness is hard to judge. Researchers have addressed the problem with more and more sophisticated static and dynamic analysis tools as an aid to assess how apps use private user data. Those tools, however, rely on the manual configuration of lists of sources of sensitive data as well as sinks which might leak data to untrusted observers. Such lists are hard to come by. We thus propose SUSI, a novel machine-learning guided approach for identifying sources and sinks directly from the code of any Android API. Given a training set of hand-annotated sources and sinks, SUSI identifies other sources and sinks in the entire API. To provide more fine-grained information, SUSI further categorizes the sources (e.g., unique identifier, location information, etc.) and sinks (e.g., network, file, etc.). For Android 4.2, SUSI identifies hundreds of sources and sinks with over 92% accuracy, many of which are missed by current information-flow tracking tools. An evaluation of about 11,000 malware samples confirms that many of these sources and sinks are indeed used. We furthermore show that SUSI can reliably classify sources and sinks even in new, previously unseen Android versions and components like Google Glass or the Chromecast API.
A stochastic approach to STDP

We present a digital implementation of the Spike Timing Dependent Plasticity (STDP) learning rule. The proposed digital implementation consists of an exponential decay (exp-decay) generator array and a STDP adaptor array. The weight values are stored in a digital memory, and the STDP adaptor w ill send these values to the exp-decay generator using a digital spike of which the duration is modulated according to these values. The exp-decay generator will then generate an exponential decay, which will be used by the STDP adaptor for performing the weight adaption. The exponential decay, which is computational expensive, is efficiently implemented by using a novel stochastic approach. This stochastic approach was fully analysed and characterised. We use a time multiplexing approach to achieve 8192 (8k) virtual STDP adaptors and exp-decay generators with only one physical adaptor and exp-decay generator respectively. We have validated our stochastic STDP approach with measurement results of a balanced excitation experiment. In that experiment, the competition (induced by STDP) between the synapses can establish a bimodal distribution of the synaptic weights: either towards zero (weak) or the maximum (strong) values. Our stochastic approach is therefore ideal for implementing the STDP learning rule in large-scale spiking neural networks running in real time.
Cost-effectively offering private buffers in SoCs and CMPs

High performance SoCs and CMPs integrate multiple cores and hardware accelerators such as network interface devices and speech recognition engines. Cores make use of SRAM organized as a cache. Accelerators make use of SRAM as special-purpose storage such as FIFOs, scratchpad memory, or other forms of private buffers. Dedicated private buffers provide benefits such as deterministic access, but are highly area inefficient due to the lower average utilization of the total available storage. We propose Buffer-integrated-Caching (BiC), which integrates private buffers and traditional caches into a single shared SRAM block. Much like shared caches improve SRAM utilization on CMPs, the BiC architecture generalizes this advantage for a heterogeneous mix of cores and accelerators in future SoCs and CMPs. We demonstrate cost-effectiveness of the BiC using SoC-based low-power servers and CMP-based servers with on-chip NIC. We show that with a small extra area added to the baseline cache, BiC removes the need for large, dedicated SRAMs, with minimal performance impact.
Interest rate pass-through in the EMU – new evidence using the nonlinear ARDL framework

This investigation puts the interest rate pass-through mechanism from policy to deposit rates in the EMU under closer scrutiny. By using the newly developed nonlinear ARDL framework of Shin et al. (2011), we are able to estimate asymmetric long-run as well as short-run coefficients. Previous studies usually assume symmetric long-run behaviour which is too restrictive according to our results. Based on fully harmonized data for three EMU countries and the EMU as a whole we disentangle short-run and long-run asymmetries as well as heterogeneity in the interest rate pass-through mechanism across EMU. Our results point to considerable asymmetries especially with regard to the long-run pass-through of money market rate changes as well as some heterogeneity between countries.
Automated Specification Discovery in a Combined Abstract Domain

Discovering program specifications automatically for heapmanipulating programs is a challenging task due to the complexity of aliasing and mutability of data structures used. This paper describes a compositional analysis framework for discovering program specifications in a combined abstract domain with shape, numerical and bag (multiset) information. The framework analyses each method and derives its summary independently from its callers. We propose a novel abstraction method with a bi-abduction technique in the combined domain to discover pre/post-conditions which cannot be automatically inferred before. The analysis does not only prove the memory safety properties, but also finds relationships between pure and shape domains towards full functional correctness of programs. A prototype of the framework has been implemented and initial experiments have shown that our approach can discover interesting properties for non-trivial programs.
PRGA: Privacy-Preserving Recording & Gateway-Assisted Authentication of Power Usage Information for Smart Grid

Smart grid network facilitates reliable and efficient power generation and transmission. The power system can adjust the amount of electricity generated based on power usage information submitted by end users. Sender authentication and user privacy preservation are two important security issues on this information flow. In this paper, we propose a scheme such that even the control center (power operator) does not know which user makes the requests of using more power or agreements of using less power until the power is actually used. At the end of each billing period (i.e., after electricity usage), the end user can prove to the power operator that it has really requested to use more power or agreed to use less power earlier. To reduce the total traffic volume in the communications network, our scheme allows gateway smart meters to help aggregate power usage information, and the power generators to determine the total amount of power that needs to be generated at different times. To reduce the impact of attacking traffic, our scheme allows gateway smart meters to help filter messages before they reach the control center. Through analysis and experiments, we show that our scheme is both effective and efficient.
Movement Detection for Safer Backward Maneuvers

Backing-up accidents are responsible for about 120 deaths and more than 6000 injuries every year in the US. IMRA-Europe has been working on a static obstacles detection system for a few years now. We propose to complement this with a system capable of detecting and localizing in real-time moving obstacles even when the vehicle is moving. This system is based on camera movement compensation using IPM (inverse perspective mapping) and probabilistic reasoning for movement detection. Using shape filtering our approach can even detect only specific types of moving objects (pedestrians, cars.). In this paper, we present the algorithms developed, show some results of the system and conclude on which future developments will bring performance improvements
Design of Combined Coverage Area Reporting and Geo-casting of Queries for Wireless Sensor Networks

In order to efficiently deal with queries or other location dependent information, it is key that the wireless sensor network informs gateways what geographical area is serviced by which gateway. The gateways are then able to e.g. efficiently route queries which are only valid in particular regions of the deployment. The proposed algorithms combine coverage area reporting and geographical routing of queries which are injected by gateways.
E-Government: An Exploratory Study of On-Iine Electronic Procurement Systems

All around the world, governments are eagerly looking toward a digital future, but their view is obstructed by the challenges they face in the modernizing such vast enterprises. This study concentrates on a Government-to-Business (G2B) relationship in which the development and implementation processes of a government-based e-procurement system are explored. This study does not represent the overall picture of building an e-government, but it helps to bring out a number of issues which are currently challenging many e-government projects and can have further implications for other projects around the world. Finally, based on the findings, we learned that implementing e-procurement system within the context of e-government is rather complex and different from a normal B2B relationship. Specifically, three issues were identified from this study: continuous technical challenge, user preferences and institutional arrangements.
Towards efficient aerodynamic shape optimization based on the Navier-Stokes equations

An accurate and efficient algorithm for Navier—Stokes aerodynamic shape optimization problems is presented. The gradient of the objective function is obtained via the discrete adjoint and flow-sensitivity methods. The adjoint and flow-sensitivity equations are solved using a preconditioned GMRES solver. A complete linearization of the discretized Navier—Stokes and turbulence model equations results in an accurate and efficient evaluation of the gradient. Furthermore, this linearization is also used to accelerate the convergence rate of the flow solver, which is based on an inexact-Newton strategy. The gradient is generally obtained in less than one third of the time required to fully converge a flow solution. Factors affecting gradient accuracy are examined. The performance of the new algorithm is demonstrated for several design problems, such as inverse design, lift-constrained drag minimization, lift enhancement and maximization of lift-to-drag ratio.
UDetect: Unsupervised Concept Change Detection for Mobile Activity Recognition

One of the major challenges in activity recognition task is the need to adapt a classification model during its operation. This is important because the underlying data distribution between those used for training and the new evolving stream of data may change during online recognition. The changes between the two sessions may occur because of differences in sensor placement, orientation and user characteristics such as age and gender. However, many of the existing approaches for model adaptation in activity recognition are blind methods because they continuously adapt the classification model without explicit detection of changes in the concepts being predicted. Therefore, we propose a concept change detection method for activity recognition under the assumption that a concept change in the model of an activity is followed by changes in the distribution of the input data attributes as well which is the realistic case for activity recognition. Our change detection method computes change detection statistic on stream of multi-dimensional unlabelled data that are classified into different concept windows. The values of the change indicators are then processed for detecting peak points that indicate concept change in the stream of activity data. Evaluation of the approach using real activity recognition dataset shows consistent detections that correlate with the error rate of the model.
The Number of Ratings Needed for Valid Pain Assessment in Clinical Trials: Replication and Extension.

OBJECTIVES To provide additional empirical findings regarding the number of pain ratings needed to obtain valid measures for assessing outcomes in pain clinical trials. DESIGN Secondary analyses of data from a clinical study examining the effects of psychological treatments on pain. Eleven adults with multiple sclerosis and chronic pain reported on four domains of pain intensity (current pain and 24-hour recalled worst, least, and average pain) on four occasions before and after receiving 16 sessions of psychological pain treatments. We evaluated the reliability and validity of four single ratings and 16 different composite scores. RESULTS Many of the single pain ratings were inadequately reliable while almost all of the composite scores, including the scores created from two ratings, evidenced adequate to excellent reliability. There was a noticeable increase in validity (ability to detect treatment effects) as the number of ratings used increased from one to two. However, there was little change in the validity as the number of items used to create composite scores increased from 2 to 3 or more. The findings also indicated that the scores assessing recalled worst pain were more valid than the scores assessing any of the other pain intensity domains. CONCLUSIONS Composite pain intensity scores created from two individual ratings of recalled pain appear to be adequately valid for detecting treatment effects. Moreover, the findings indicate that the selection of the pain intensity domain to use as a primary outcome variable may play a more important role than increasing reliability by obtaining more assessments; specifically, ratings of recalled worst pain may be more valid for detecting treatment effects than ratings of average pain.
An information retrieval approach to identifying infrequent events in surveillance video

This paper presents work on integrating multiple computer vision-based approaches to surveillance video analysis to support user retrieval of video segments showing human activities. Applied computer vision using real-world surveillance video data is an extremely challenging research problem, independently of any information retrieval (IR) issues. Here we describe the issues faced in developing both generic and specific analysis tools and how they were integrated for use in the new TRECVid interactive surveillance event detection task. We present an interaction paradigm and discuss the outcomes from face-to-face end user trials and the resulting feedback on the system from both professionals, who manage surveillance video, and computer vision or machine learning experts. We propose an information retrieval approach to finding events in surveillance video rather than solely relying on traditional annotation using specifically trained classifiers.
Part-of-speech tagging models for parsing

We investigate the accuracy of alternative part-of-speech tag models and their impact on parser performance. In addition to considering single-tag and multipletag per word input, tag selection models which draw on information available from the parser are applied. Results indicate that given a ‘good’ PoS tagger, parserbased tag selection models are unable to improve on the low tag error rates of the tagger. Multiple-tag per word input can improve on parser accuracy but at a cost to efficiency. A dynamic tag selection model is also applied, which only increases the number of tags considered for sentences if a full parse could not be found. This achieves the best accuracy and provides a means to overcome the trade-off between tag error rates and increased parse ambiguity introduced by multiple-tag per word input.
XENON 1T Demonstrator Report

Observation suggests ∼80% of all the mass of the universe currently comes from a nonluminous, nonbaryonic form of matter known as dark matter. Currently, WIMPs are one of the most favored models to explain the missing mass of the universe. XENON100, a detector experiment in Gran Sasso, has place the best upper limits for the cross section at ∼ 10−44cm2 for a 50GeV/c mass WIMP. Since detection of the WIMP remains elusive the next generation LXe experiment, XENON1T, will have a 1 tonne fiducial volume and has a projected sensitivity to ∼ 10−47cm2 for a 50GeV/c mass WIMP. In order for the next generation experiment to run efficiently and to gain the required level of purity the current gas system is inadequate. Work on the demonstrator apparatus has made significant progress towards the development of a highly efficient gas system for XENON1T. Through the chacterization of the internal pressures, addition of a heat exchanger, new liquid-gas phase bell design and automated data analysis necessary groundwork has been laid out for the next generation LXe dark matter experiment.