nils-herrmann/clean_scirepeval huggingface dataset:
An Approximate Minimum Degree Ordering Algorithm

An Approximate Minimum Degree ordering algorithm (AMD) for preordering a symmetric sparse matrix prior to numerical factorization is presented. We use techniques based on the quotient graph for matrix factorization that allow us to obtain computationally cheap bounds for the minimum degree. We show that these bounds are often equal to the actual degree. The resulting algorithm is typically much faster than previous minimum degree ordering algorithms, and produces results that are comparable in quality with the best orderings from other minimum degree algorithms. ENSEEIHT-IRIT, Toulouse, France. . Computer and Information Sciences Department University of Florida, Gainesville, Florida, USA. phone: (904) 392-1481, email: davis@cis.ufl.edu. Technical reports and matrices are available via the World Wide Web at http://www.cis.ufl.edu/̃ davis, or by anonymous ftp at ftp.cis.ufl.edu:cis/tech-reports. Support for this project was provided by the National Science Foundation (ASC-9111263 and DMS-9223088). Portions of this work were supported by a post-doctoral grant from CERFACS. Rutherford Appleton Laboratory, Chilton, Didcot, Oxon. 0X11 0QX England, and European Center for Research and Advanced Training in Scientific Computation (CERFACS), Toulouse, France. . Technical reports, information on the Harwell Subroutine Library, and matrices are available via the World Wide Web at http://www.cis.rl.ac.uk/struct/ARCD/NUM.html, or by anonymous ftp at seamus.cc.rl.ac.uk/pub.
Large-Scale Learnable Graph Convolutional Networks

Convolutional neural networks (CNNs) have achieved great success on grid-like data such as images, but face tremendous challenges in learning from more generic data such as graphs. In CNNs, the trainable local filters enable the automatic extraction of high-level features. The computation with filters requires a fixed number of ordered units in the receptive fields. However, the number of neighboring units is neither fixed nor are they ordered in generic graphs, thereby hindering the applications of convolutional operations. Here, we address these challenges by proposing the learnable graph convolutional layer (LGCL). LGCL automatically selects a fixed number of neighboring nodes for each feature based on value ranking in order to transform graph data into grid-like structures in 1-D format, thereby enabling the use of regular convolutional operations on generic graphs. To enable model training on large-scale graphs, we propose a sub-graph training method to reduce the excessive memory and computational resource requirements suffered by prior methods on graph convolutions. Our experimental results on node classification tasks in both transductive and inductive learning settings demonstrate that our methods can achieve consistently better performance on the Cora, Citeseer, Pubmed citation network, and protein-protein interaction network datasets. Our results also indicate that the proposed methods using sub-graph training strategy are more efficient as compared to prior approaches.
Measuring, analyzing and predicting security vulnerabilities in software systems

In this work we examine the feasibility of quantitatively characterizing some aspects of security. In particular, we investigate if it is possible to predict the number of vulnerabilities that can potentially be present in a software system but may not have been found yet. We use several major operating systems as representatives of complex software systems. The data on vulnerabilities discovered in these systems are analyzed. We examine the results to determine if the density of vulnerabilities in a program is a useful measure. We also address the question about what fraction of software defects are security related, i.e., are vulnerabilities. We examine the dynamics of vulnerability discovery hypothesizing that it may lead us to an estimate of the magnitude of the undiscovered vulnerabilities still present in the system. We consider the vulnerability discovery rate to see if models can be developed to project future trends. Finally, we use the data for both commercial and opensource systems to determine whether the key observations are generally applicable. Our results indicate that the values of vulnerability densities fall within a range of values, just like the commonly used measure of defect density for general defects. Our examination also reveals that it is possible to model the vulnerability discovery using a logistic model that can sometimes be approximated by a linear model. a 2006
Coexistence measurements and analysis of IEEE 802.15.4 with Wi-Fi and bluetooth for vehicle networks

This paper gives an overview of the channel access methods of three wireless technologies that are likely to be used in the environment of vehicle networks: IEEE 802.15.4, IEEE 802.11 and Bluetooth. Researching the coexistence of IEEE 802.15.4 with IEEE 802.11 and Bluetooth, results of experiments conducted in a radio frequency anechoic chamber are presented. The power densities of the technologies on a single IEEE 802.15.4 channel are compared. It is shown that the pure existence of an IEEE 802.11 access point leads to collisions due to different timing scales. Furthermore, the packet drop rate caused by Bluetooth is analyzed and an estimation formula for it is given.
Representing Big Data as Networks: New Methods and Insights

by Jian Xu Our world produces massive data every day; they exist in diverse forms, from pairwise data and matrix to time series and trajectories. Meanwhile, we have access to the versatile toolkit of network analysis. Networks also have different forms; from simple networks to higher-order network, each representation has different capabilities in carrying information. For researchers who want to leverage the power of the network toolkit, and apply it beyond networks data to sequential data, diffusion data, and many more, the question is: how to represent big data and networks? This dissertation makes a first step to answering the question. It proposes the higherorder network, which is a critical piece for representing higher-order interaction data; it introduces a scalable algorithm for building the network, and visualization tools for interactive exploration. Finally, it presents broad applications of the higher-order network in the real-world. Dedicated to those who strive to be better persons.
AN ENHANCED FREQUENT PATTERN GROWTH BASED ON MAP REDUCE FOR MINING ASSOCIATION RULES

In mining frequent itemsets, one of most important algorithm is FP-growth. FP-growth proposes an algorithm to compress information needed for mining frequent itemsets in FP-tree and recursively constructs FP-trees to find all frequent itemsets. In this paper, we propose the EFP-growth (enhanced FPgrowth) algorithm to achieve the quality of FP-growth. Our proposed method implemented the EFPGrowth based on MapReduce framework using Hadoop approach. New method has high achieving performance compared with the basic FP-Growth. The EFP-growth it can work with the large datasets to discovery frequent patterns in a transaction database. Based on our method, the execution time under different minimum supports is decreased..
A hybrid modulation method for lifetime extension of power semiconductors in wind power converters

Pulse width modulation (PWM) techniques can be classified into continuous pulse width modulation (CPWM) and discontinuous pulse width modulation (DPWM) types. The switching loss of power devices in DPWM converters is lower than that in CPWM converters. Lower loss could reduce the junction temperature fluctuation in converters of wind turbine generator system (WTGS) and may result in longer power devices lifetime. However, employing DPWM scheme under all WTGS operation conditions will lead to power quality concern. To solve this problem, a new hybrid modulation scheme which combines the CPWM and DPWM methods for WTGS converters is presented in this paper. In the presented hybrid modulation method, two modulation schemes are switched back and forth according to the wind speed in the wind farm site. The performance of the presented modulation scheme is verified and compared with that of other PWM schemes through a case study of 1.2 MW WTGS in long-term mission profiles. The results show that the lifetime of power devices with the presented hybrid approach is longer than that with the CPWM, and is shorter than that with the DPWMs. Moreover, the power quality of the power converters with the hybrid modulation scheme can be guaranteed in all operation conditions, which may not be achieved with DPWMs.
Sequential Data Fusion of GNSS Pseudoranges and Dopplers With Map-Based Vision Systems

Tightly coupling GNSS pseudorange and Doppler measurements with other sensors is known to increase the accuracy and consistency of positioning information. Nowadays, high-accuracy geo-referenced lane marking maps are seen as key information sources in autonomous vehicle navigation. When an exteroceptive sensor such as a video camera or a lidar is used to detect them, lane markings provide positioning information which can be merged with GNSS data. In this paper, measurements from a forward-looking video camera are merged with raw GNSS pseudoranges and Dopplers on visible satellites. To create a localization system that provides pose estimates with high availability, dead reckoning sensors are also integrated. The data fusion problem is then formulated as sequential filtering. A reduced-order state space modeling of the observation problem is proposed to give a real-time system that is easy to implement. A Kalman filter with measured input and correlated noises is developed using a suitable error model of the GNSS pseudoranges. Our experimental results show that this tightly coupled approach performs better, in terms of accuracy and consistency, than a loosely coupled method using GNSS fixes as inputs.
Methodology to Analyze the Accuracy of 3D Objects Reconstructed with Collaborative Robot Based Monocular LSD-SLAM

SLAM systems are mainly applied for robot navigation while research on feasibility for motion planning with SLAM for tasks like bin-picking, is scarce. Accurate 3D reconstruction of objects and environments is important for planning motion and computing optimal gripper pose to grasp objects. In this work, we propose the methods to analyze the accuracy of a 3D environment reconstructed using a LSD-SLAM system with a monocular camera mounted onto the gripper of a collaborative robot. We discuss and propose a solution to the pose space conversion problem. Finally, we present several criteria to analyze the 3D reconstruction accuracy. These could be used as guidelines to improve the accuracy of 3D reconstructions with monocular LSD-SLAM and other SLAM based solutions.
The long-term management and outcomes of cloacal anomalies

Cloacal anomalies occur when failure of the urogenital septum to separate the cloacal membrane results in the urethra, vagina, rectum and anus opening into a single common channel. The reported incidence is 1:50,000 live births. Short-term paediatric outcomes of surgery are well reported and survival into adulthood is now usual, but long-term outcome data are less comprehensive. Chronic renal failure is reported to occur in 50 % of patients with cloacal anomalies, and 26–72 % (dependant on the length of the common channel) of patients experience urinary incontinence in adult life. Defaecation is normal in 53 % of patients, with some managed by methods other than surgery, including medication, washouts, stoma and antegrade continent enema. Gynaecological anomalies are common and can necessitate reconstructive surgery at adolescence for menstrual obstruction. No data are currently available on sexual function and little on the quality of life. Pregnancy is extremely rare and highly risky. Patient care should be provided by a multidisciplinary team with experience in managing these and other related complex congenital malformations. However, there is an urgent need for a well-planned, collaborative multicentre prospective study on the urological, gastrointestinal and gynaecological aspects of this rare group of complex conditions.
Meta Structures in Knowledge Graphs

This paper investigates meta structures, schema-level graphs that abstract connectivity information among a set of entities in a knowledge graph. Meta structures are useful in a variety of knowledge discovery tasks ranging from relatedness explanation to data retrieval. We formalize the meta structure computation problem and devise efficient automata-based algorithms. We introduce a meta structure-based relevance measure, which can retrieve entities related to those in input. We implemented our machineries in a visual tool called MEKoNG. We report on an extensive experimental evaluation, which confirms the suitability of our proposal from both the efficiency and effectiveness point of view.
On Distributed Fuzzy Decision Trees for Big Data

Fuzzy decision trees (FDTs) have shown to be an effective solution in the framework of fuzzy classification. The approaches proposed so far to FDT learning, however, have generally neglected time and space requirements. In this paper, we propose a distributed FDT learning scheme shaped according to the MapReduce programming model for generating both binary and multiway FDTs from big data. The scheme relies on a novel distributed fuzzy discretizer that generates a strong fuzzy partition for each continuous attribute based on fuzzy information entropy. The fuzzy partitions are, therefore, used as an input to the FDT learning algorithm, which employs fuzzy information gain for selecting the attributes at the decision nodes. We have implemented the FDT learning scheme on the Apache Spark framework. We have used ten real-world publicly available big datasets for evaluating the behavior of the scheme along three dimensions: 1) performance in terms of classification accuracy, model complexity, and execution time; 2) scalability varying the number of computing units; and 3) ability to efficiently accommodate an increasing dataset size. We have demonstrated that the proposed scheme turns out to be suitable for managing big datasets even with a modest commodity hardware support. Finally, we have used the distributed decision tree learning algorithm implemented in the MLLib library and the Chi-FRBCS-BigData algorithm, a MapReduce distributed fuzzy rule-based classification system, for comparative analysis.
Surveying Position Based Routing Protocols for Wireless Sensor and Ad-hoc Networks

A focus of the scientific community is to design network oriented position-based routing protocols and this has resulted in a very high number of algorithms, different in approach and performance and each suited only to particular applications. However, though numerous, very few position-based algorithms have actually been adopted for commercial purposes. This article is a survey of almost 50 position-based routing protocols and it comes as an aid in the implementation of this type of routing in various applications which may need to consider the advantages and pitfalls of position-based routing. An emphasis is made on geographic routing, whose notion is clarified as a more restrictive and more efficient type of position-based routing. The protocols are therefore divided into geographic and non-geographic routing protocols and each is characterized according to a number of network design issues and presented in a comparative manner from multiple points of view. The main requirements of current general applications are also studied and, depending on these, the survey proposes a number of protocols for use in particular application areas. This aims to help both researchers and potential users assess and choose the protocol best suited to their interest.
Temperature-related development and population parameters for Drosophila suzukii (Diptera: Drosophilidae) on cherry and blueberry.

Temperature-related studies were conducted on Drosophila suzukii Matsumura (Diptera: Drosophilidae: Drosophilini). From 10-28°C, temperature had a significant impact on blueberries, Vaccinium corymbosum L. (Ericales: Ericaceae), and cherries, Prunus avium (L.) L. 1755 (Rosales: Rosaceae), important commercial hosts of D. suzukii. Temperature had a significant influence on D. suzukii developmental period, survival, and fecundity, with decreasing developmental periods as temperatures increased to 28°C. At 30°C, the highest temperature tested, development periods increased, indicating that above this temperature the developmental extremes for the species were approached. D. suzukii reared on blueberries had lower fecundity than reared on cherries at all temperatures where reproduction occurred. The highest net reproductive rate (R(o)) and intrinsic rate of population increase (r(m)) were recorded on cherries at 22°C and was 195.1 and 0.22, respectively. Estimations using linear and nonlinear fit for the minimum, optimal, and maximum temperatures where development can take place were respectively, 7.2, 28.1, and 42.1°C. The r(m) values were minimal, optimal, and maximal at 13.4, 21.0, and 29.3°C, respectively. Our laboratory cultures of D. suzukii displayed high rates of infection for Wolbachia spp.  Population estimates using the model explained different risk levels during the key cherry harvest period between these seasons.
SPANNER: A Self-Repairing Spiking Neural Network Hardware Architecture

Recent research has shown that a glial cell of astrocyte underpins a self-repair mechanism in the human brain, where spiking neurons provide direct and indirect feedbacks to presynaptic terminals. These feedbacks modulate the synaptic transmission probability of release (PR). When synaptic faults occur, the neuron becomes silent or near silent due to the low PR of synapses; whereby the PRs of remaining healthy synapses are then increased by the indirect feedback from the astrocyte cell. In this paper, a novel hardware architecture of Self-rePAiring spiking Neural NEtwoRk (SPANNER) is proposed, which mimics this self-repairing capability in the human brain. This paper demonstrates that the hardware can self-detect and self-repair synaptic faults without the conventional components for the fault detection and fault repairing. Experimental results show that SPANNER can maintain the system performance with fault densities of up to 40%, and more importantly SPANNER has only a 20% performance degradation when the self-repairing architecture is significantly damaged at a fault density of 80%.
A compact dual frequency antenna with Sierpinski gasket based slots

A compact dual band microstrip patch antenna with fractal based slots is proposed in this paper. The structure consists of a square patch with modified Sierpinski gasket based slots, electromagnetically fed by a microstrip line. The antenna resonates at two distinct frequencies with linear polarization in the same plane. The simulated current distribution over the antenna establishes that the higher resonance is due to the smaller slots and the lower due to the central slot. In addition to the dual frequency behaviour, the antenna exhibits a size reduction of 77% at the lower resonance. Also it has broad radiation characteristics with cross-polarization levels better than -20dB.
Are the studies on cancer risk from CT scans biased by indication? Elements of answer from a large-scale cohort study in France

Recent epidemiological results suggested an increase of cancer risk after receiving computed tomography (CT) scans in childhood or adolescence. Their interpretation is questioned due to the lack of information about the reasons for examination. Our objective was to estimate the cancer risk related to childhood CT scans, and examine how cancer-predisposing factors (PFs) affect assessment of the radiation-related risk.The cohort included 67 274 children who had a first scan before the age of 10 years from 2000 to 2010 in 23 French departments. Cumulative X-rays doses were estimated from radiology protocols. Cancer incidence was retrieved through the national registry of childhood cancers; PF from discharge diagnoses.During a mean follow-up of 4 years, 27 cases of tumours of the central nervous system, 25 of leukaemia and 21 of lymphoma were diagnosed; 32% of them among children with PF. Specific patterns of CT exposures were observed according to PFs. Adjustment for PF reduced the excess risk estimates related to cumulative doses from CT scans. No significant excess risk was observed in relation to CT exposures.This study suggests that the indication for examinations, whether suspected cancer or PF management, should be considered to avoid overestimation of the cancer risks associated with CT scans.
Single or multiple frequency generators in on-going brain activity: A mechanistic whole-brain model of empirical MEG data

During rest, envelopes of band-limited on-going MEG signals co-vary across the brain in consistent patterns, which have been related to resting-state networks measured with fMRI. To investigate the genesis of such envelope correlations, we consider a whole-brain network model assuming two distinct fundamental scenarios: one where each brain area generates oscillations in a single frequency, and a novel one where each brain area can generate oscillations in multiple frequency bands. The models share, as a common generator of damped oscillations, the normal form of a supercritical Hopf bifurcation operating at the critical border between the steady state and the oscillatory regime. The envelopes of the simulated signals are compared with empirical MEG data using new methods to analyse the envelope dynamics in terms of their phase coherence and stability across the spectrum of carrier frequencies. Considering the whole-brain model with a single frequency generator in each brain area, we obtain the best fit with the empirical MEG data when the fundamental frequency is tuned at 12Hz. However, when multiple frequency generators are placed at each local brain area, we obtain an improved fit of the spatio-temporal structure of on-going MEG data across all frequency bands. Our results indicate that the brain is likely to operate on multiple frequency channels during rest, introducing a novel dimension for future models of large-scale brain activity.
Enhanced Immersion in Augmented Reality Applications

This thesis will examine and evaluate different mechanics that could be used in games using augmented reality. Augmented reality, the technology used to integrate computer-generated images with the real world environment, allows developers to enhance a user’s gaming experience. The different mechanics will focus on immersion and on user engagement and examine which of the two is more important in games. This is examined by implementing the different mechanics in an application for a Google Tango tablet. Immersion is created by letting the environment act on virtual objects, via occlusion culling. The virtual agent interacts with the real world to generate engagement. The different methods are surveyed online, and user tests performed with the application. The results showed how the concept of combining the surveyed methods of generating immersion and engagement using augmented reality was successful.
Efficient sum of absolute difference computation on FPGAs

An improved architecture for efficiently computing the sum of absolute differences (SAD) on FPGAs is proposed in this work. It is based on a configurable adder/subtractor implementation in which each adder input can be negated at runtime. The negation of both inputs at the same time is explicitly allowed and used to compute the sum of absolute values in a single adder stage. The architecture can be mapped to modern FPGAs from Xilinx and Altera. An analytic complexity model as well as synthesis experiments yield an average look-up table (LUT) reduction of 17.4% for an input word size of 8 bit compared to state-of-the-art. As the SAD computation is a resource demanding part in image processing applications, the proposed circuit can be used to replace the SAD core of many applications to enhance their efficiency.