nils-herrmann/clean_scirepeval huggingface dataset:
A Distributed Parameter Electromechanical Model for Cantilevered Piezoelectric Energy Harvesters

Cantilevered beams with piezoceramic layers have been frequently used as piezoelectric vibration energy harvesters in the past five years. The literature includes several single degree-of-freedom models, a few approximate distributed parameter models and even some incorrect approaches for predicting the electromechanical behavior of these harvesters. In this paper, we present the exact analytical solution of a cantilevered piezoelectric energy harvester with Euler–Bernoulli beam assumptions. The excitation of the harvester is assumed to be due to its base motion in the form of translation in the transverse direction with small rotation, and it is not restricted to be harmonic in time. The resulting expressions for the coupled mechanical response and the electrical outputs are then reduced for the particular case of harmonic behavior in time and closed-form exact expressions are obtained. Simple expressions for the coupled mechanical response, voltage, current, and power outputs are also presented for excitations around the modal frequencies. Finally, the model proposed is used in a parametric case study for a unimorph harvester, and important characteristics of the coupled distributed parameter system, such as short circuit and open circuit behaviors, are investigated in detail. Modal electromechanical coupling and dependence of the electrical outputs on the locations of the electrodes are also discussed with examples.
Kernel Entropy Component Analysis

We introduce kernel entropy component analysis (kernel ECA) as a new method for data transformation and dimensionality reduction. Kernel ECA reveals structure relating to the Renyi entropy of the input space data set, estimated via a kernel matrix using Parzen windowing. This is achieved by projections onto a subset of entropy preserving kernel principal component analysis (kernel PCA) axes. This subset does not need, in general, to correspond to the top eigenvalues of the kernel matrix, in contrast to the dimensionality reduction using kernel PCA. We show that kernel ECA may produce strikingly different transformed data sets compared to kernel PCA, with a distinct angle-based structure. A new spectral clustering algorithm utilizing this structure is developed with positive results. Furthermore, kernel ECA is shown to be an useful alternative for pattern denoising.
Ku-Band Low-Profile Rx-only and Tx-Rx antennas for Mobile Satellite Communications

This paper presents two different Ku-Band Low-Profile antenna concepts for Mobile Satellite Communications. The antennas are based on low-cost hybrid mechanical-electronic steerable solutions but, while the first one allows a broadband reception of a satellite signal (Receive-only antenna concept), the second one provides transmit and receive functions for a bi-directional communication link between the satellite and the mobile user terminal (Transmit-Receive antenna). Both examples are suitable for integration in land vehicles and aircrafts.
Assisted suicide by oxygen deprivation with helium at a Swiss right-to-die organisation.

In Switzerland, right-to-die organisations assist their members with suicide by lethal drugs, usually barbiturates. One organisation, Dignitas, has experimented with oxygen deprivation as an alternative to sodium pentobarbital.



To analyse the process of assisted suicide by oxygen deprivation with helium and a common face mask and reservoir bag.



This study examined four cases of assisted suicide by oxygen deprivation using helium delivered via a face mask. Videos of the deaths were provided by the Zurich police. Dignitas provided protocol and consent information.



One man and three women were assisted to death by oxygen deprivation. There was wide variation in the time to unconsciousness and the time to death, probably due to the poor mask fit. Swiss law prevented attendants from effectively managing the face mask apparatus. Purposeless movements of the extremities were disconcerting for Dignitas attendants, who are accustomed to assisting suicide with barbiturates. None of the dying individuals attempted self-rescue.



The dying process of oxygen deprivation with helium is potentially quick and appears painless. It also bypasses the prescribing role of physicians, effectively demedicalising assisted suicide. Oxygen deprivation with a face mask is not acceptable because leaks are difficult to control and it may not eliminate rebreathing. These factors will extend time to unconsciousness and time to death. A hood method could reduce the problem of mask fit. With a hood, a flow rate of helium sufficient to provide continuous washout of expired gases would remedy problems observed with the mask.
A piece of the action: Modulation of sensory-motor regions by action idioms and metaphors

The idea that the conceptual system draws on sensory and motor systems has received considerable experimental support in recent years. Whether the tight coupling between sensory-motor and conceptual systems is modulated by factors such as context or task demands is a matter of controversy. Here, we tested the context sensitivity of this coupling by using action verbs in three different types of sentences in an fMRI study: literal action, apt but non-idiomatic action metaphors, and action idioms. Abstract sentences served as a baseline. The result showed involvement of sensory-motor areas for literal and metaphoric action sentences, but not for idiomatic ones. A trend of increasing sensory-motor activation from abstract to idiomatic to metaphoric to literal sentences was seen. These results support a gradual abstraction process whereby the reliance on sensory-motor systems is reduced as the abstractness of meaning as well as conventionalization is increased, highlighting the context sensitive nature of semantic processing.
WordPress security: an analysis based on publicly available exploits

The danger of SQL injections has been known for more than a decade but injection attacks have led the OWASP top 10 for years and still are one of the major reasons for devastating attacks on web sites. As about 24% percent of the top 10 million web sites are built upon the content management system WordPress, it's no surprise that content management systems in general and WordPress in particular are frequently targeted. To understand how the underlying security bugs can be discovered and exploited by attackers, 199 publicly disclosed SQL injection exploits for WordPress and its plugins have been analyzed. The steps an attacker would take to uncover and utilize these bugs are followed in order to gain access to the underlying database through automated, dynamic vulnerability scanning with well-known, freely available tools. Previous studies have shown that the majority of the security bugs are caused by the same programming errors as 10 years ago and state that the complexity of finding and exploiting them has not increased significantly. Furthermore, they claim that although the complexity has not increased, automated tools still do not detect the majority of bugs. The results of this paper show that tools for automated, dynamic vulnerability scanning only play a subordinate role for developing exploits. The reason for this is that only a small percentage of attack vectors can be found during the detection phase. So even if the complexity of exploiting an attack vector has not increased, this attack vector has to be found in the first place, which is the major challenge for this kind of tools. Therefore, from today's perspective, a combination with manual and/or static analysis is essential when testing for security vulnerabilities.
The RRADS platform: a real road autonomous driving simulator

This platform paper introduces a methodology for simulating an autonomous vehicle on open public roads. The paper outlines the technology and protocol needed for running these simulations, and describes an instance where the Real Road Autonomous Driving Simulator (RRADS) was used to evaluate 3 prototypes in a between-participant study design. 35 participants were interviewed at length before and after entering the RRADS. Although our study did not use overt deception---the consent form clearly states that a licensed driver is operating the vehicle---the protocol was designed to support suspension of disbelief. Several participants who did not read the consent form clearly strongly believed that they were interacting with a fully autonomous vehicle.
 The RRADS platform provides a lens onto the attitudes and concerns that people in real-world autonomous vehicles might have, and also points to ways that a protocol deliberately using misdirection can gain ecologically valid reactions from study participants.
Convolutional neural network for vehicle detection in low resolution traffic videos

Recent works on Convolutional Neural Network (CNN) in object detection and identification show its superior performance over other systems. It is being used on several machine vision tasks such as in face detection, OCR and traffic monitoring. These systems, however, use high resolution images which contain significant pattern information as compared to the typical cameras, such as for traffic monitoring, which are low resolution, thus, suffer low SNR. This work investigates the performance of CNN in detection and classification of vehicles using low quality traffic cameras. Results show an average accuracy equal to 94.72% is achieved by the system. An average of 51.28 ms execution time for a 2GHz CPU and 22.59 ms execution time for NVIDIA Fermi GPU are achieved making the system applicable to be implemented in real-time using 4-input traffic video with 6 fps.
SEE: Towards Semi-Supervised End-to-End Scene Text Recognition

Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. In recent years several new systems that try to solve at least one of the two sub-tasks (text detection and text recognition) have been proposed. In this paper we present SEE, a step towards semi-supervised neural networks for scene text detection and recognition, that can be optimized end-to-end. Most existing works consist of multiple deep neural networks and several pre-processing steps. In contrast to this, we propose to use a single deep neural network, that learns to detect and recognize text from natural images, in a semi-supervised way. SEE is a network that integrates and jointly learns a spatial transformer network, which can learn to detect text regions in an image, and a text recognition network that takes the identified text regions and recognizes their textual content. We introduce the idea behind our novel approach and show its feasibility, by performing a range of experiments on standard benchmark datasets, where we achieve competitive results.
Protocol Analysis and Expert Thought : Concurrent Verbalizations of Thinking during Experts ’ Performance on Representative Tasks

The superior skills of experts, such as accomplished musicians and chess masters, can be amazing to most spectators. For example, club-level chess players are often puzzled by the chess moves of grandmasters and world champions. Similarly, many recreational athletes find it inconceivable that most other adults – regardless of the amount or type of training – have the potential ever to reach the performance levels of international competitors. Especially puzzling to philosophers and scientists has been the question of the extent to which expertise requires innate gifts versus specialized acquired skills and abilities. One of the most widely used and simplest methods of gathering data on exceptional performance is to interview the experts themselves. But are experts always capable of describing their thoughts, their behaviors, and their strategies in a manner that would allow less-skilled individuals to understand how the experts do what they do, and perhaps also understand how they might reach expert level through appropriate training? To date, there has been considerable controversy over the extent to which experts are capable of explaining the nature and structure of their exceptional performance. Some pioneering scientists, such as Binet (1893 / 1966), questioned the validity of the experts’ descriptions when they found that some experts gave reports inconsistent with those of other experts. To make matters worse, in those rare cases that allowed verification of the strategy by observing the performance, discrepancies were found between the reported strategies and the observations (Watson, 1913). Some of these discrepancies were explained, in part, by the hypothesis that some processes were not normally mediated by awareness/attention and that the mere act of engaging in self-observation (introspection) during performance changed the content of ongoing thought processes. These problems led most psychologists in first half of the 20th century to reject all types of introspective verbal reports as valid scientific evidence, and they focused almost exclusively on observable behavior (Boring, 1950). In response to the problems with the careful introspective analysis of images and perceptions, investigators such as John B.
High gain printed antenna array for FMCW radar at 17 GHz

We are presenting the design of a high gain printed antenna array consisting of 420 identical patch antennas intended for FMCW radar at Ku band. The array exhibits 3 dB-beamwidths of 2° and 10° in H and E plane, respectively, side lobe suppression better than 20 dB, gain about 30 dBi and VSWR less than 2 in the frequency range 17.1 - 17.6 GHz. Excellent antenna efficiency that is between 60 and 70 % is achieved by proper impedance matching throughout the array and by using series feeding architecture with both resonant and traveling-wave feed. Enhanced cross polarization suppression is obtained by anti-phase feeding of the upper and the lower halves of the antenna. Overall antenna dimensions are 31 λ0 × 7.5 λ0.
User access mode selection in fog computing based radio access networks

Fog computing based radio access network is a promising paradigm for the fifth generation wireless communication system to provide high spectral and energy efficiency. With the help of the new designed fog computing based access points (F-APs), the user-centric objectives can be achieved through the adaptive technique and will relieve the load of fronthaul and alleviate the burden of base band unit pool. In this paper, we derive the coverage probability and ergodic rate for both F-AP users and device-to-device users by taking into account the different nodes locations, cache sizes as well as user access modes. Particularly, the stochastic geometry tool is used to derive expressions for above performance metrics. Simulation results validate the accuracy of our analysis and we obtain interesting tradeoffs that depend on the effect of the cache size, user node density, and the quality of service constrains on the different performance metrics.
Utility functions in autonomic systems

Utility functions provide a natural and advantageous framework for achieving self-optimization in distributed autonomic computing systems. We present a distributed architecture, implemented in a realistic prototype data center, that demonstrates how utility functions can enable a collection of autonomic elements to continually optimize the use of computational resources in a dynamic, heterogeneous environment. Broadly, the architecture is a two-level structure of independent autonomic elements that supports flexibility, modularity, and self-management. Individual autonomic elements manage application resource usage to optimize local service-level utility functions, and a global arbiter allocates resources among application environments based on resource-level utility functions obtained from the managers of the applications. We present empirical data that demonstrate the effectiveness of our utility function scheme in handling realistic, fluctuating Web-based transactional workloads running on a Linux cluster.
Slip compensation for a Mars rover

A system that enables continuous slip compensation for a Mars rover has been designed, implemented, and field-tested. This system is composed of several components that allow the rover to accurately and continuously follow a designated path, compensate for slippage, and reach intended goals in high-slip environments. These components include: visual odometry, vehicle kinematics, a Kalman filter pose estimator, and a slip compensation/path follower. Visual odometry tracks distinctive scene features in stereo imagery to estimate rover motion between successively acquired stereo image pairs. The vehicle kinematics for a rocker-bogie suspension system estimates motion by measuring wheel rates, and rocker, bogie, and steering angles. The Kalman filter merges data from an inertial measurement unit (IMU) and visual odometry. This merged estimate is then compared to the kinematic estimate to determine how much slippage has occurred, taking into account estimate uncertainties. If slippage has occurred then a slip vector is calculated by differencing the current Kalman filter estimate from the kinematic estimate. This slip vector is then used to determine the necessary wheel velocities and steering angles to compensate for slip and follow the desired path.
Multi-Label Learning with Posterior Regularization

In many multi-label learning problems, especially as the number of labels grow, it is challenging to gather completely annotated data. This work presents a new approach for multi-label learning from incomplete annotations. The main assumption is that because of label correlation, the true label matrix as well as the soft predictions of classifiers shall be approximately low rank. We introduce a posterior regularization technique which enforces soft constraints on the classifiers, regularizing them to prefer sparse and low-rank predictions. Avoiding strict lowrank constraints results in classifiers which better fit the real data. The model can be trained efficiently using EM and stochastic gradient descent. Experiments in both the image and text domains demonstrate the contributions of each modeling assumption and show that the proposed approach achieves state-of-the-art performance on a number of challenging datasets.
Optimal Real-Time Bidding Frameworks Discussion

This note is a complementary material for the solution of optimal real-time bidding function in paper , where the estimated cost is taken as the bid price, i.e., the upper bound of the true cost. Here we discuss a more general bid optimisation framework with various utility and cost functions. 1 Preliminaries Define the bidding function as b(r) which returns the bid price given the estimated clickthrough rate (CTR) r of an ad impression. Winning probability. Given the market price distribution pz(z) and the bid price b, the probability of winning the auction is
A Full-360$^{\circ}$ Reflection-Type Phase Shifter With Constant Insertion Loss

A new reflection-type phase shifter with a full 360deg relative phase shift range and constant insertion loss is presented. This feature is obtained by incorporating a new cascaded connection of varactors into the impedance-transforming quadrature coupler. The required reactance variation of a varactor can be reduced by controlling the impedance ratio of the quadrature coupler. The implemented phase shifter achieves a measured maximal relative phase shift of 407deg, an averaged insertion loss of 4.4 dB and return losses better than 19 dB at 2 GHz. The insertion-loss variation is within plusmn0.1 and plusmn0.2 dB over the 360deg and 407deg relative phase shift tuning range, respectively.
Uplink power efficiency of multiuser MIMO with very large antenna arrays

A multiplicity of autonomous terminals simultaneously transmits data streams to a compact array of antennas. The array uses imperfect channel-state information derived from transmitted pilots to extract the individual data streams. The power radiated by the terminals can be made inversely proportional to the square-root of the number of base station antennas with no reduction in performance. In contrast if perfect channel-state information were available the power could be made inversely proportional to the number of antennas. A maximum-ratio combining receiver normally performs worse than a zero-forcing receiver. However as power levels are reduced, the cross-talk introduced by the inferior maximum-ratio receiver eventually falls below the noise level and this simple receiver becomes a viable option.
Automatic Extraction of Social Networks from Literary Text: A Case Study on Alice in Wonderland

In this paper we present results for two tasks: social event detection and social network extraction from a literary text, Alice in Wonderland. For the first task, our system trained on a news corpus using tree kernels and support vector machines beats the baseline systems by a statistically significant margin. Using this system we extract a social network from Alice in Wonderland. We show that while we achieve an F-measure of about 61% on social event detection, our extracted unweighted network is not statistically distinguishable from the un-weighted gold network according to popularly used network measures.
Creating GUI Testing Tools Using Accessibility Technologies

Since manual black-box testing of GUI-based APplications(GAPs) is tedious and laborious, and existing toolsdo not fully address different aspects of the testing process, test engineers create custom testing tools to automate the testing process. These tools interact with GAPs by performing actions on their GUI objects. An extra effort that test engineers put in writing test tools is paid off when these tools are run repeatedly on different GAPs. Unfortunately, creating custom GUI testing tools is a laborious and intellectually intensive process, during which test engineers use platform-specific libraries and techniques. As a result, these tools are expensive, difficult to maintain and evolve, and they often run only on specific platforms.We offer a universal approach for creating custom testingGUI tools. This approach is lightweight, portable, nonintrusive, universal, and cheap, and it combines a nonstandard use of accessibility technologies for accessing and controlling GAPs in a uniform way with a visualization mechanism that enables test personnel to interact with GUI objects by performing point-and-click, drag-and-drop operations on GAPs. We describe how we used this approach to create various GUI testing tools, delve into technical features of accessibility technologies, and review our experience with this approach.
Convolutional Nonlinear Neighbourhood Components Analysis for Time Series Classification

During last decade, tremendous efforts have been devoted to the research of time series classification. Indeed, many previous works suggested that the simple nearest-neighbor classification is effective and difficult to beat. However, we usually need to determine the distance metric (e.g., Euclidean distance and Dynamic Time Warping) for different domains, and current evidence shows that there is no distance metric that is best for all time series data. Thus, the choice of distance metric has to be done empirically, which is time expensive and not always effective. To automatically determine the distance metric, in this paper, we investigate the distance metric learning and propose a novel Convolutional Nonlinear Neighbourhood Components Analysis model for time series classification. Specifically, our model performs supervised learning to project original time series into a transformed space. When classifying, nearest neighbor classifier is then performed in this transformed space. Finally, comprehensive experimental results demonstrate that our model can improve the classification accuracy to some extent, which indicates that it can learn a good distance metric.