nils-herrmann/clean_scirepeval huggingface dataset:
Security in next generation air traffic communication networks

A multitude of wireless technologies are used by air traffic communication systems during different flight phases. From a conceptual perspective, all of them are insecure as security was never part of their design and the evolution of wireless security in aviation did not keep up with the state of the art. Recent contributions from academic and hacking communities have exploited this inherent vulnerability and demonstrated attacks on some of these technologies. However, these inputs revealed that a large discrepancy between the security perspective and the point of view of the aviation community exists. In this thesis, we aim to bridge this gap and combine wireless security knowledge with the perspective of aviation professionals to improve the safety of air traffic communication networks. To achieve this, we develop a comprehensive new threat model and analyse potential vulnerabilities, attacks, and countermeasures. Since not all of the required aviation knowledge is codified in academic publications, we examine the relevant aviation standards and also survey 242 international aviation experts. Besides extracting their domain knowledge, we analyse the awareness of the aviation community concerning the security of their wireless systems and collect expert opinions on the potential impact of concrete attack scenarios using insecure technologies. Based on our analysis, we propose countermeasures to secure air traffic communication that work transparently alongside existing technologies. We discuss, implement, and evaluate three different approaches based on physical and data link layer information obtained from live aircraft. We show that our countermeasures are able to defend against the injection of false data into air traffic control systems and can significantly and immediately improve the security of air traffic communication networks under the existing real-world constraints. Finally, we analyse the privacy consequences of open air traffic control protocols. We examine sensitive aircraft movements to detect large-scale events in the real world and illustrate the futility of current attempts to maintain privacy for aircraft owners.
History and principles of Shack-Hartmann wavefront sensing.

developed out of a need to solve a problem. The problem was posed, in the late 1960s, to the Optical Sciences Center (OSC) at the University of Arizona by the US Air Force. They wanted to improve the images of satellites taken from earth. The earth's atmosphere limits the image quality and exposure time of stars and satellites taken with telescopes over 5 inches in diameter at low altitudes and 10 to 12 inches in diameter at high altitudes. Dr. Aden Mienel was director of the OSC at that time. He came up with the idea of enhancing images of satellites by measuring the Optical Transfer Function (OTF) of the atmosphere and dividing the OTF of the image by the OTF of the atmosphere. The trick was to measure the OTF of the atmosphere at the same time the image was taken and to control the exposure time so as to capture a snapshot of the atmospheric aberrations rather than to average over time. The measured wavefront error in the atmosphere should not change more than ␭/10 over the exposure time. The exposure time for a low earth orbit satellite imaged from a mountaintop was determined to be about 1/60 second. Mienel was an astronomer and had used the standard Hartmann test , where large wooden or cardboard panels were placed over the aperture of a large telescope. The panels had an array of holes that would allow pencils of rays from stars to be traced through the telescope system. A photographic plate was placed inside and outside of focus, with a sufficient separation, so the pencil of rays would be separated from each other. Each hole in the panel would produce its own blurry image of the star. By taking two images a known distance apart and measuring the centroid of the images, one can trace the rays through the focal plane. Hartmann used these ray traces to calculate figures of merit for large telescopes. The data can also be used to make ray intercept curves (H'-tan U'). When Mienel could not cover the aperture while taking an image of the satellite, he came up with the idea of inserting a beam splitter in collimated space behind the eyepiece and placing a plate with holes in it at the image of the pupil. Each hole would pass a pencil of rays to a vidicon tube (this was before …
A passive weight compensation mechanism with a non-circular pulley and a spring

We propose a new weight compensation mechanism with a non-circular pulley and a spring. We show the basic principle and numerical design method to derive the shape of the non-circular pulley. After demonstration of the weight compensation for an inverted/ordinary pendulum system, we extend the same mechanism to a parallel five-bar linkage system, analyzing the required torques using transposed Jacobian matrices. Finally, we develop a three degree of freedom manipulator with relatively small output actuators and verified that the weight compensation mechanism significantly contributes to decrease static torques to keep the same posture within manipulator's work space.
Cloudlet load balancing in wireless metropolitan area networks

With advances in wireless communication technology, more and more people depend heavily on portable mobile devices for businesses, entertainments and social interactions. Although such portable mobile devices can offer various promising applications, their computing resources remain limited due to their portable size. This however can be overcome by remotely executing computation-intensive tasks on clusters of near by computers known as cloudlets. As increasing numbers of people access the Internet via mobile devices, it is reasonable to envision in the near future that cloudlet services will be available for the public through easily accessible public wireless metropolitan area networks (WMANs). However, the outdated notion of treating cloudlets as isolated data-centers-in-a-box must be discarded as there are clear benefits to connecting multiple cloudlets together to form a network. In this paper we investigate how to balance the workload between multiple cloudlets in a network to optimize mobile application performance. We first introduce a system model to capture the response times of offloaded tasks, and formulate a novel optimization problem, that is to find an optimal redirection of tasks between cloudlets such that the maximum of the average response times of tasks at cloudlets is minimized. We then propose a fast, scalable algorithm for the problem. We finally evaluate the performance of the proposed algorithm through experimental simulations. The experimental results demonstrate the significant potential of the proposed algorithm in reducing the response times of tasks.
Sentiment Analysis in Turkish : Towards a Complete Framework

Sentiment analysis has attracted a lot of research interest in recent years, especially in the context of social media. While most of this research has focused on English, there is ample data and interest in the topic for many other languages, as well. In this article we propose a comprehensive sentiment analysis system for Turkish. Our contributions include addressing linguistic issues such as negation and intensification, as well as covering different levels of sentiment analysis such as aspect, sentence, and document levels. We evaluated our methodology on Turkish movie reviews and obtained accuracies ranging from 60% to 79% in ternary and binary classification tasks at different levels of analysis.
Deep Quaternion Networks

The field of deep learning has seen significant advancement in recent years. However, much of the existing work has been focused on real-valued numbers. Recent work has shown that a deep learning system using the complex numbers can be deeper for a fixed parameter budget compared to its real-valued counterpart. In this work, we explore the benefits of generalizing one step further into the hyper-complex numbers, quaternions specifically, and provide the architecture components needed to build deep quaternion networks. We develop the theoretical basis by reviewing quaternion convolutions, developing a novel quaternion weight initialization scheme, and developing novel algorithms for quaternion batch-normalization. These pieces are tested in a classification model by end-to-end training on the CIFAR −10 and CIFAR −100 data sets and a segmentation model by end-to-end training on the KITTI Road Segmentation data set. These quaternion networks show improved convergence compared to real-valued and complex-valued networks, especially on the segmentation task, while having fewer parameters.
Using Multi-Feature and Classifier Ensembles to Improve Malware Detection

With the rapid growth of internet application, malware has become one of the major threats to information security. Traditionally, anti-virus products use signature matching to detect malware, but the drawback is that they can not detect new and unknown malware. Recent studies showed that the use of machine learning can successfully detect new and unknown malware, but the limitation of this technique is its high false rate. The performance of machine learning is influenced by two main factors: (1) the features used to represent the instances; and (2) the algorithm used to generate classifier. In this paper, we improved the accuracy of machine learning from these two factors. On the one hand we combined features extracted from both content-based and behavior-based analyses to represent the instances; on the other hand, we used classifier ensembles to replace individual classifier. Based on our methodology, a hybrid-classifier was implemented to classify unknown executables as either malicious or benign. Experimental results show that the methods proposed in this paper can improve the accuracy of malware detection effectively.
Analysis, Experimental Results, and Range Adaptation of Magnetically Coupled Resonators for Wireless Power Transfer

Wireless power technology offers the promise of cutting the last cord, allowing users to seamlessly recharge mobile devices as easily as data are transmitted through the air. Initial work on the use of magnetically coupled resonators for this purpose has shown promising results. We present new analysis that yields critical insight into the design of practical systems, including the introduction of key figures of merit that can be used to compare systems with vastly different geometries and operating conditions. A circuit model is presented along with a derivation of key system concepts, such as frequency splitting, the maximum operating distance (critical coupling), and the behavior of the system as it becomes undercoupled. This theoretical model is validated against measured data and shows an excellent average coefficient of determination of 0.9875. An adaptive frequency tuning technique is demonstrated, which compensates for efficiency variations encountered when the transmitter-to-receiver distance and/or orientation are varied. The method demonstrated in this paper allows a fixed-load receiver to be moved to nearly any position and/or orientation within the range of the transmitter and still achieve a near-constant efficiency of over 70% for a range of 0-70 cm.
The narrative constitution of identity : A relational and network approach

justice. Women, by contrast, were believed to be at a lower stage because they were found to have a sense of agency still tied primarily to their social relationships and to make political and moral decisions based on context-specific principles based on these relationships rather than on the grounds of their own autonomous judgments. Students of gender studies know well just how busy social scientists have been kept by their efforts to come up with ever more sociological "alibis" for the question of why women did not act like men. Gilligan's response was to refuse the terms of the debate altogether. She thus did not develop yet another explanation for why women are "deviant." Instead, she turned the question on its head by asking what was wrong with the theory a theory whose central premises defines 50% of social beings as "abnormal." Gilligan translated this question into research by subjecting the abstraction of universal and discrete agency to comparative research into female behavior evaluated on its own terms The new research revealed women to be more "concrete" in their thinking and more attuned to "fairness" while men acted on "abstract reasoning" and "rules of justice." These research findings transformed female otherness into variation and difference but difference now freed from the normative de-
Density-based Denoising of Point Cloud

Point cloud source data for surface reconstruction is usually contaminated with noise and outliers. To overcome this deficiency, a density-based point cloud denoising method is presented to remove outliers and noisy points. First, particle-swam optimization technique is employed for automatically approximating optimal bandwidth of multivariate kernel density estimation to ensure the robust performance of density estimation. Then, mean-shift based clustering technique is used to remove outliers through a thresholding scheme. After removing outliers from the point cloud, bilateral mesh filtering is applied to smooth the remaining points. The experimental results show that this approach, comparably, is robust and efficient.
Techniques for Automatically Correcting Words in Text

Research aimed at correcting words in text has focused on three progressively more difficult problems:(1) nonword error detection; (2) isolated-word error correction; and (3) context-dependent work correction. In response to the first problem, efficient pattern-matching and n-gram analysis techniques have been developed for detecting strings that do not appear in a given word list. In response to the second problem, a variety of general and application-specific spelling correction techniques have been developed. Some of them were based on detailed studies of spelling error patterns. In response to the third problem, a few experiments using natural-language-processing tools or statistical-language models have been carried out. This article surveys documented findings on spelling error patterns, provides descriptions of various nonword detection and isolated-word error correction techniques, reviews the state of the art of context-dependent word correction techniques, and discusses research issues related to all three areas of automatic error correction in text.
Vienna-Rectifier-Based Direct Torque Control of PMSG for Wind Energy Application

Vienna rectifier as generator-side converter of wind energy conversion system (WECS) using a permanent-magnet synchronous generator (PMSG) has several advantages compared to conventional back-to-back inverter, such as higher efficiency and improved total harmonic distortion. On the other hand, direct torque control (DTC) of the generator in WECS is of interest, particularly for low-power applications due to several merits, including fast torque response, insensitivity to PMSG model and associated parameters, elimination of rotor position sensor, and reduced computations. In this paper, the effects of Vienna rectifier voltage vectors on instantaneous PMSG torque and stator flux are derived, and DTC of PMSG by the Vienna rectifier is implemented, considering the constraints imposed by the Vienna rectifier. Experimental tests for a 1-kW prototype are presented, which confirm the simulations and theoretical results.
Advanced Multifrequency Radar Instrumentation for Polar Research

This paper presents a radar sensor package specifically developed for wide-coverage sounding and imaging of polar ice sheets from a variety of aircraft. Our instruments address the need for a reliable remote sensing solution well-suited for extensive surveys at low and high altitudes and capable of making measurements with fine spatial and temporal resolution. The sensor package that we are presenting consists of four primary instruments and ancillary systems with all the associated antennas integrated into the aircraft to maintain aerodynamic performance. The instruments operate simultaneously over different frequency bands within the 160 MHz-18 GHz range. The sensor package has allowed us to sound the most challenging areas of the polar ice sheets, ice sheet margins, and outlet glaciers; to map near-surface internal layers with fine resolution; and to detect the snow-air and snow-ice interfaces of snow cover over sea ice to generate estimates of snow thickness. In this paper, we provide a succinct description of each radar and associated antenna structures and present sample results to document their performance. We also give a brief overview of our field measurement programs and demonstrate the unique capability of the sensor package to perform multifrequency coincidental measurements from a single airborne platform. Finally, we illustrate the relevance of using multispectral radar data as a tool to characterize the entire ice column and to reveal important subglacial features.
The roles of habit and web site quality in e-commerce

Previous research has primarily examined consumers’ perceived usefulness of web sites and trust in the web retailer as two major predictors of web site use and e-commerce adoption. While the consumers’ repeated behavior in the past (i.e., habit) may contribute to continuance behavior, it has not been investigated. This article includes habit as a primary construct along with perceived usefulness and trust to predict and explain consumers’ continued behavior of using a B2C web site. Additionally, included are several web quality measures as antecedents to trust and perceived usefulness. The research model is evaluated using structural equation modeling. Results show that consumers’ behavioral intentions to continue using a B2C web site are determined by all three key drivers: perceived usefulness, trust, and habit. Furthermore, not all dimensions of web quality have a significant effect on perceived usefulness and trust.
Hierarchical Gaussian process latent variable models

The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we extend the GP-LVM through hierarchies. A hierarchical model (such as a tree) allows us to express conditional independencies in the data as well as the manifold structure. We first introduce Gaussian process hierarchies through a simple dynamical model, we then extend the approach to a more complex hierarchy which is applied to the visualisation of human motion data sets.
Biodiesel fuel production by transesterification of oils.

Biodiesel (fatty acid methyl esters), which is derived from triglycerides by transesterification with methanol, has attracted considerable attention during the past decade as a renewable, biodegradable, and nontoxic fuel. Several processes for biodiesel fuel production have been developed, among which transesterification using alkali-catalysis gives high levels of conversion of triglycerides to their corresponding methyl esters in short reaction times. This process has therefore been widely utilized for biodiesel fuel production in a number of countries. Recently, enzymatic transesterification using lipase has become more attractive for biodiesel fuel production, since the glycerol produced as a by-product can easily be recovered and the purification of fatty methyl esters is simple to accomplish. The main hurdle to the commercialization of this system is the cost of lipase production. As a means of reducing the cost, the use of whole cell biocatalysts immobilized within biomass support particles is significantly advantageous since immobilization can be achieved spontaneously during batch cultivation, and in addition, no purification is necessary. The lipase production cost can be further lowered using genetic engineering technology, such as by developing lipases with high levels of expression and/or stability towards methanol. Hence, whole cell biocatalysts appear to have great potential for industrial application.
Honeypot detection in advanced botnet attacks

Botnets have become one of the major attacks in the internet today due to their illicit profitable financial gain. Meanwhile, honeypots have been successfully deployed in many computer security defence systems. Since honeypots set up by security defenders can attract botnet compromises and become spies in exposing botnet membership and botnet attacker behaviours, they are widely used by security defenders in botnet defence. Therefore, attackers constructing and maintaining botnets will be forced to find ways to avoid honeypot traps. In this paper, we present a hardware and software independent honeypot detection methodology based on the following assumption: security professionals deploying honeypots have a liability constraint such that they cannot allow their honeypots to participate in real attacks that could cause damage to others, while attackers do not need to follow this constraint. Attackers could detect honeypots in their botnets by checking whether compromised machines in a botnet can successfully send out unmodified malicious traffic. Based on this basic detection principle, we present honeypot detection techniques to be used in both centralised botnets and Peer-to-Peer (P2P) structured botnets. Experiments show that current standard honeypots and honeynet programs are vulnerable to the proposed honeypot detection techniques. At the end, we discuss some guidelines for defending against general honeypot-aware attacks.
Software Language Identification with Natural Language Classifiers

Software language identification techniques are applicable to many situations from universal IDE support to legacy code analysis. Most widely used heuristics are based on software artefact metadata such as file extensions or on grammar-based text analysis such as keyword search. In this paper we propose to use statistical language models from the natural language processing field such as n-grams, skip-grams, multinominal naïve Bayes and normalised compression distance. Our preliminary experiments show that some of these models used as classifiers can achieve high precision and recall and can be used to properly identify language families, languages and even deal with embedded code fragments.
Detecting, Tracking and Counting Fish in Low Quality Unconstrained Underwater Videos

In this work a machine vision system capable of analysing underwater videos for detecting, tracking and counting fish is presented. The real-time videos, collected near the Ken-Ding sub-tropical coral reef waters are managed by EcoGrid, Taiwan and are barely analysed by marine biologists. The video processing system consists of three subsystems: the video texture analysis, fish detection and tracking modules. Fish detection is based on two algorithms computed independently, whose results are combined in order to obtain a more accurate outcome. The tracking was carried out by the application of the CamShift algorithm that enables the tracking of objects whose numbers may vary over time. Unlike existing fish-counting methods, our approach provides a reliable method in which the fish number is computed in unconstrained environments and under several scenarios (murky water, algae on camera lens, moving plants, low contrast, etc.). The proposed approach was tested with 20 underwater videos, achieving an overall accuracy as high as 85%.