nils-herrmann/clean_scirepeval huggingface dataset:
Real-time event-driven spiking neural network object recognition on the SpiNNaker platform

This paper presents a real-time spiking neural network adaptation of the HMAX object recognition model on an event-driven platform. Visual input is provided by a spiking silicon retina, while the SpiNNaker system is used as a computational hardware platform for implementation. We show the implementation of a simple Leaky Integrate-and-Fire (LIF) neuron model on SpiNNaker to create an event driven network, where a neuron only updates when it receives an interrupt indicating that a new input spike has been received. The model output consists of view tuned neurons which respond selectively to a particular view of an object. The network can be used to discriminate between objects, or between the same object at different views. On a 26 class character recognition task, the correct class is always assigned the highest probability (69.42% on average).
Tooth survival following non-surgical root canal treatment: a systematic review of the literature.

To investigate (i) the effect of study characteristics on reported tooth survival after root canal treatment (RCTx) and (ii) the effect of clinical factors on the proportion of root filled teeth surviving after RCTx.



Longitudinal human clinical studies investigating tooth survival after RCTx which were published up to the end of 2007 were identified electronically (MEDLINE and Cochrane database 1966-2007 December, week 4). In addition, four journals (Dental Traumatology, , bibliographies of all relevant articles and review articles were hand searched. Two reviewers (Y-LN, KG) assessed and selected the studies based on specified inclusion criteria and extracted the data onto a pre-designed proforma, independently. The criteria were as follows: (i) clinical study on RCTx; (ii) stratified analysis of primary and secondary RCTx available; (iii) sample size given and larger than 10; (iv) at least 6-month postoperative review; (v) success based on survival of tooth; and (vi) proportion of teeth surviving after treatment given or could be calculated from the raw data. Three strands of evidence or analyses were used to triangulate a consensus view. The reported findings from individual studies, including those excluded for quantitative analysis, were utilized for the intuitive synthesis, which constituted the first strand of evidence. Secondly, the pooled weighted proportion of teeth surviving and thirdly the combined effects of potential prognostic factors were estimated using the fixed and random effects meta-analyses on studies fulfilling all the inclusion criteria.



Of the 31 articles identified, 14 studies published between 1993 and 2007 were included. The majority of studies were retrospective (n = 10) and only four prospective. The pooled percentages of reported tooth survival over 2-3, 4-5 and 8-10 years following RCTx were 86% (95% CI: 75%, 98%), 93% (95% CI: 92%, 94%) and 87% (95% CI: 82%, 92%), respectively. Substantial differences in study characteristics were found to hinder effective direct comparison of findings. Evidence for the effect of prognostic factors on tooth survival was weak. Based on the data available for meta-analyses, four conditions were found to significantly improve tooth survival. In descending order of influence, the conditions increasing observed proportion of survival were as follows: (i) a crown restoration after RCTx; (ii) tooth having both mesial and distal proximal contacts; (iii) tooth not functioning as an abutment for removable or fixed prosthesis; and (iv) tooth type or specifically non-molar teeth. Statistical heterogeneity was substantial in some cases but its source could not be investigated because of insufficient available information.



The pooled proportion of teeth surviving over 2-10 years following RCTx ranged between 86% and 93%. Four factors (listed above) were identified as significant prognostic factors with concurrence between all three strands of evidence.
Non-invasive measurement of blood flow using magnetic disturbance method

Current laser Doppler method of blood flow sensing requires optical contact to the skin, tend to be bulky and have performance subjective to body fluids (e.g. blood, perspiration) and environmental contaminants (e.g. mud, water). This paper proposes a novel method of noninvasive acquisition of blood flow by measuring the magnetic disturbance created due to blood flowing through a localized magnetic field. The proposed system employs a GMR based magnetic sensor and magnet of 3 mm radius, placed on a major blood vessel. The magnetic field generated by the magnet acts both as the biasing field for the sensor and also the uniform magnetic flux for blood flow disturbance. As such, the system is compact, operates at room temperature and is able to sense through clothing. The signal acquired from the magnetic and optical methods are compared using the post-occlusive reactive hyperaemia test, where measurement results on 6 different healthy subjects are found to have error of less than 5%, showing the successful use of the magnetic method to measure blood flow.
A Quantitative Study of Data in the NLP community

We present results on a quantitative analysis of publications in the NLP domain on collecting, publishing and availability of research data. We find that a wide range of publications rely on data crawled from the web, but few give details on how potentially sensitive data was treated. Additionally, we find that while links to repositories of data are given, they often do not work even a short time after publication. We put together several suggestions on how to improve this situation based on publications from the NLP domain, but also other research areas.
Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques

The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM). TIHM is a technology assisted monitoring system that uses Internet of Things (IoT) enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients' routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%.
Predicting accuracy on large datasets from smaller pilot data

Because obtaining training data is often the most difficult part of an NLP or ML project, we develop methods for predicting how much data is required to achieve a desired test accuracy by extrapolating results from systems trained on a small pilot training dataset. We model how accuracy varies as a function of training size on subsets of the pilot data, and use that model to predict how much training data would be required to achieve the desired accuracy. We introduce a new performance extrapolation task to evaluate how well different extrapolations predict system accuracy on larger training sets. We show that details of hyperparameter optimisation and the extrapolation models can have dramatic effects in a document classification task. We believe this is an important first step in developing methods for estimating the resources required to meet specific engineering performance targets.
Linux kernel infrastructure for user-level device drivers

Linux 2.5.x has good support now for user-mode device drivers — XFree being the biggest and most obvious — but also there is support for user-mode input devices and for devices that hang off the parallel port. The motivations for user-mode device drivers are many: • Ease of development (all the normal user-space tools can be used to write and debug, not restricted to use of C only (could use Java, C++, Perl, etc), fewer reboot cycles needed, fewer restrictions on what one can do with respect to reentrancy and interrupts, etc., etc.) • Ease of deployment (kernel ↔ user interfaces change much more slowly than in-kernel interfaces; no licensing issues; no need for end-users to recompile to get module versions right, etc., etc.)) • Increased robustness (less likely that a buggy driver can cause a panic) • Increased functionality. Some things are just plain easier to do in user space than in the kernel — e.g., networking. • Increased simplicity (rather than have, say, a generic IDE controller that has to understand the quirks of many different kinds of controllers and drivers, you can afford to pick at run time the controller you really need) There are however some drawbacks, the main ones being performance and security. Three recent developments have made it possible to implement an infrastructure for user-level device drivers that perform almost as well (in some cases better than) in-kernel device drivers. These are 1. the new pthreads library (and corresponding kernel changes: futexes, faster clone and exit, etc); 2. fast system call support; and 3. IOMMUs. Now that many high-end machines have an IOMMU, it becomes possible, at least in theory, to provide secure access to DMA to user processes. Fast system calls allow the kernel ↔ user crossing to be extremely cheap, making user-process interrupt handling feasible. And fast context-switching and IPC for Posix threads, means that multi-threaded device drivers can have the kind of performance that until recently was only available in-kernel. ∗This work was funded by HP, NICTA, the ARC, and the University of NSW through the Gelato programme
Polysemy: Theoretical and Computational Approaches Yael Ravin and Claudia Leacock (editors) (IBM T. J. Watson Research Center and Educational Testing Services) New York: Oxford University Press, 2000, xi+227 pp; hardbound, ISBN 0-19-823842-8, $74.00, £45.00; paperbound, ISBN 0-19-925086-3, $21.95, £

As the editors of this volume remind us, polysemy has been a vexing issue for the understanding of language since antiquity. For half a century, it has been a major bottleneck for natural language processing. It contributed to the failure of early machine translation research (remember Bar-Hillel’s famous pen and box example) and is still plaguing most natural language processing and information retrieval applications. A recent issue of this journal described the state of the art in automatic sense disambiguation (Ide and Véronis 1998), and Senseval system competitions have revealed the immense difficulty of the task (http://www.sle.sharp.co.uk/senseval2). However, no significant progress can be made on the computational aspects of polysemy without serious advances in theoretical issues. At the same time, theoretical work can be fostered by computational results and problems, and language-processing applications can provide a unique test bed for theories. It was therefore an excellent idea to gather both theoretical and applied contributions in the same book. Yael Ravin and Claudia Leacock are well-known names to those who work on the theoretical and computational aspects of word meaning. In this volume, they bring together a collection of essays from leading researchers in the field. As far as I can tell, these essays are not reprints or expanded versions of conference papers, as is often the case for edited works; instead, they seem to have been specially commissioned for the purposes of this book, which makes it even more exciting to examine. The book is composed of 11 chapters. It is not formally divided into parts, but chapters dealing more specifically with the computational aspects of polysemy are grouped together at the end (and constitute about one-third of the volume). Chapter 1 is an overview written by the volume editors. Yael Ravin and Claudia Leacock survey the main theories of meaning and their treatment of polysemy. These include the classical Aristotelian approach revived by Katz and Fodor (1963); Rosch’s (1977) prototypical approach, which has its roots in Wittgenstein’s Philosophical Investigations (1953); and the relational approach recently exemplified by WordNet (Fellbaum 1998), which (although the authors do not mention it) can be traced back to Peirce’s (1931–1958) and Selz’s (1913, 1922) graphs and which gained popularity with Quillian’s (1968) semantic networks. In the course of this overview, Ravin and Leacock put the individual chapters into perspective by relating them to the various theories.
Image Steganography: A Review

Steganography can be defined as the study of invisible communication that usually deals with the ways of hiding the existence of the communicated message. If it is achieved successfully, the message does not attract attention from eavesdroppers and attackers. The main objectives of steganography are undetectability, robustness (resistance to various image processing methods and compression) and capacity of the hidden data. These are the main factors which make it different from other techniques watermarking and cryptography. This paper includes the important steganography methods and the main focus is on the review of steganography in digital images. Adaptive steganography, Frequency domain, Image steganography, Spatial domain, Steganography.
A user-centric evaluation framework for recommender systems

This research was motivated by our interest in understanding the criteria for measuring the success of a recommender system from users' point view. Even though existing work has suggested a wide range of criteria, the consistency and validity of the combined criteria have not been tested. In this paper, we describe a unifying evaluation framework, called ResQue (Recommender systems' Quality of user experience), which aimed at measuring the qualities of the recommended items, the system's usability, usefulness, interface and interaction qualities, users' satisfaction with the systems, and the influence of these qualities on users' behavioral intentions, including their intention to purchase the products recommended to them and return to the system. We also show the results of applying psychometric methods to validate the combined criteria using data collected from a large user survey. The outcomes of the validation are able to 1) support the consistency, validity and reliability of the selected criteria; and 2) explain the quality of user experience and the key determinants motivating users to adopt the recommender technology. The final model consists of thirty two questions and fifteen constructs, defining the essential qualities of an effective and satisfying recommender system, as well as providing practitioners and scholars with a cost-effective way to evaluate the success of a recommender system and identify important areas in which to invest development resources.
Image processing on multinode hadoop cluster

In the past few years the data produced all over the internet has increased with an exponential rate. The storage costs have been rising immensely. But, in the field of Computer Science, the introduction of new technologies has resulted in reduction of storage costs. This led to a rampant rise in the data generation rates. This huge amount of data that is so vast and complex such that classical methods are insufficient for processing is termed as ‘Big Data’. There are various tools in Hadoop to analyze the textual data such as Pig, HBase, etc. But the data present on the Internet as well as the Social Networking sites comprises of unstructured media. The maximum spectrum of the media files is covered by Image data. The major concern is not about the storage of images, but the processing of images being generated with the speed of light. Every day around 350 million pictures are being uploaded on social network. Until now, over 200 billion photos have been uploaded only on Facebook. This accounts to an average of around 200 photos per user. This whole amount of data generated round the globe can be classified into three formats-Structured, Semi-Structured, and Unstructured. The Image data contains not only the pictures but also the data defining those pictures such as the resolution, source of the image, capture device, etc. To fetch all this information in a structured format HIPI (Hadoop Image Processing Interface) tools are used. In this paper image processing is performed on a MultiNode Hadoop Cluster and its performance is measured.
Feature Reduction for Document Clustering and Classification

Often users receive search results which contain a wide range of documents, only some of which are relevant to their information needs. To address this problem, ever more systems not only locate information for users, but also organise that information on their behalf. We look at two main automatic approaches to information organisation: interactive clustering of search results and pre-categorising documents to provide hierarchical browsing structures. To be feasible in real world applications, both of these approaches require accurate yet efficient algorithms. Yet, both suffer from the curse of dimensionality — documents are typically represented by hundreds or thousands of words (features) which must be analysed and processed during clustering or classification. In this paper, we discuss feature reduction techniques and their application to document clustering and classification, showing that feature reduction improves efficiency as well as accuracy. We validate these algorithms using human relevance assignments and categorisation.
Driver intent inference at urban intersections using the intelligent driver model

Predicting turn and stop maneuvers of potentially errant drivers is a basic requirement for advanced driver assistance systems for urban intersections. Previous work has shown that an early estimate of the driver's intent can be inferred by evaluating the vehicle's speed during the intersection approach. In the presence of a preceding vehicle, however, the velocity profile might be dictated by car-following behaviour rather than by the need to slow down before doing a left or right turn. To infer the driver's intent under such circumstances, a simple, real-time capable approach using an explicit model to represent both car-following and turning behaviour is proposed. Models for typical turning behavior are extracted from real world data. Preliminary results based on a Bayes net classification are presented.
Wire Speed Name Lookup: A GPU-based Approach

This paper studies the name lookup issue with longest prefix matching, which is widely used in URL filtering, content routing/switching, etc. Recently Content-Centric Networking (CCN) has been proposed as a clean slate future Internet architecture to naturally fit the contentcentric property of today’s Internet usage: instead of addressing end hosts, the Internet should operate based on the identity/name of contents. A core challenge and enabling technique in implementing CCN is exactly to perform name lookup for packet forwarding at wire speed. In CCN, routing tables can be orders of magnitude larger than current IP routing tables, and content names are much longer and more complex than IP addresses. In pursuit of conquering this challenge, we conduct an implementation-based case study on wire speed name lookup, exploiting GPU’s massive parallel processing power. Extensive experiments demonstrate that our GPU-based name lookup engine can achieve 63.52M searches per second lookup throughput on large-scale name tables containing millions of name entries with a strict constraint of no more than the telecommunication level 100μs per-packet lookup latency. Our solution can be applied to contexts beyond CCN, such as search engines, content filtering, and intrusion prevention/detection.  Qunfeng Dong (qunfeng.dong@gmail.com) and Prof. Bin Liu (lmyujie@gmail.com), placed in alphabetic order, are the correspondence authors of the paper. Yi Wang and Yuan Zu, placed in alphabetic order, are the lead student authors of Tsinghua University and University of Science and Technology of China, respectively. This paper is supported by 863 project (2013AA013502), NSFC (61073171, 61073184), Tsinghua University Initiative Scientific Research Program(20121080068), the Specialized Research Fund for the Doctoral Program of Higher Education of China(20100002110051), the Ministry of Education (MOE) Program for New Century Excellent Talents (NCET) in University, the Science and Technological Fund of Anhui Province for Outstanding Youth (10040606Y05), by the Fundamental Research Funds for the Central Universities (WK0110000007, WK0110000019), and Jiangsu Provincial Science Foundation (BK2011360).
CMOS analog circuit design via geometric programming

This talk presents a new method for optimizing and automating component sizing in CMOS analog circuits. It is shown that a wide variety of circuit performance measures have a special form, i.e., they are posynomial functions of the design variables. As a result, circuit design problems can be posed as geometric programs, a special type of convex optimization problem for which very efficient global optimization methods exist. The synthesis method is therefore fast, and determines the globally optimal design; in particular, the final solution is completely independent of the starting point, and infeasible specifications are unambiguously detected. Also, because the method is highly efficient, in practice it can be used to carry out robust designs and quickly explore the design space. We show that despite the restricted form of geometric programs, a large variety of circuit design problems can be posed as geometric programs.
Minimum Steiner Tree Construction

In optimizing the area of Very Large Scale Integrated (VLSI) layouts, circuit interconnections should generally be realized with minimum total interconnect. This chapter addresses several variations of the corresponding fundamental Steiner minimal tree (SMT) problem, where a given set of pins is to be connected using minimum total wirelength. Steiner trees are important in global routing and wirelength estimation [15], as well as in various non-VLSI applications such as phylogenetic tree reconstruction in biology [48], network routing [61], and civil engineering, among many other areas [21, 25, 26, 29, 51, 74].