nils-herrmann/clean_scirepeval huggingface dataset:
Opinion Mining: Aspect Level Sentiment Analysis using SentiWordNet and Amazon Web Services

In today's linked world, users can purchase items at any time. However, in online shopping sites customers can locate their concerned product by visiting the site of the trader directly or by seeking among different vendors by using a shopping search engine, which demonstrate the similar product’s accessibility and costing at alternative e-retailers. The active progress of the audience of shopping sites on the internet lead to the development of these resources as a new origin of the public’s mood and opinion about particular product. The tracking of public’s responses through reviews and feedbacks in online shopping sites has attracted a greater level of enthusiasm in the research society. Researchers notice that the millions of public opinion polls can’t be processed manually. This figure out the requirement of computerized methods for intelligent analysis of text instructions, which allows to process a large amount of data in short time and to interpret customer’s feedbacks. This interpretation of feedback is the most valuable and complicated element of the computerized processing. These notions provide the opportunity to perform large-scale research and to observe Online shopping sited in real-time. The main focus of this paper is to determine the aspect terms present in each sentence,
Missing data techniques in analogy-based software development effort estimation

Missing Data (MD) is a widespread problem that can affect the ability to use data to construct effective software development effort prediction systems. This paper investigates the use of missing data (MD) techniques with two analogy-based software development effort estimation techniques: Classical Analogy and Fuzzy Analogy. More specifically, we analyze the predictive performance of these two analogy-based techniques when using toleration, deletion or k-nearest neighbors (KNN) imputation techniques. A total of 1,512 experiments were conducted involving seven data sets, three MD techniques (toleration, deletion and KNN imputation), three missingness mechanisms (MCAR: missing completely at random, MAR: missing at random, NIM: non-ignorable missing), and MD percentages from 10 percent to 90 percent. The results suggest that Fuzzy Analogy generates more accurate estimates in terms of the Standardized Accuracy measure (SA) than Classical Analogy regardless of the MD technique, the data set used, the missingness mechanism or the MD percentage. Moreover, this study found that the use of KNN imputation, rather than toleration or deletion, may improve the prediction accuracy of both analogy-based techniques. However, toleration, deletion and KNN imputation are affected by the missingness mechanism and the MD percentage, both of which have a strong negative impact upon effort prediction accuracy.
The Light Verb Jungle

The study of light verbs and complex predicates is frought wi th dangers and misunderstandings that go beyond the merely terminological. This paper attemp s to pic through the terminological, theoretical and empirical jungle in order to arrive at a nove l understanding of the role of light verbs crosslinguistically. In particular, this paper addresses how light verbs and complex predicates can be identified crosslinguistically, what the relationsh ip between the two is, and whether light verbs must always be associated with uniform syntactic and s emantic properties. Finally, the paper proposes a novel view of how light verbs are situated in the le xicon by addressing some historical data and their relationship with preverbs and verb particle s. Jespersen (1965,Volume VI:117) is generally credited with first coining the termlight verb, which he applied to English V+NP constructions as in (1).
Fully Automated Road Defect Detection Using Street View Images

Road quality assessment is a crucial part in municipalities' work to maintain their infrastructure, plan upgrades, and manage their budgets. Properly maintaining this infrastructure relies heavily on consistently monitoring its condition and deterioration over time. This can be a challenge, especially in larger towns and cities where there is a lot of city property to keep an eye on. We review road quality assessment methods currently employed, and then describe our novel system, which integrates a collection of existing algorithms, aimed at identifying distressed road regions from street view images and pinpointing cracks within them. We predict distressed regions by computing Fisher vectors on local SIFT descriptors and classifying them with an SVM trained to distinguish between road qualities. We follow this step with a comparison to a weighed contour map within these distressed regions to identify exact crack and defect locations, and use the contour weights to predict the crack severity. Promising results are obtained on our manually annotated dataset, which indicate the viability of using this cost-effective system to perform road quality assessment at the municipal level.
A hierarchy of evidence for assessing qualitative health research.

The objective of this study is to outline explicit criteria for assessing the contribution of qualitative empirical studies in health and medicine, leading to a hierarchy of evidence specific to qualitative methods.



This paper arose from a series of critical appraisal exercises based on recent qualitative research studies in the health literature. We focused on the central methodological procedures of qualitative method (defining a research framework, sampling and data collection, data analysis, and drawing research conclusions) to devise a hierarchy of qualitative research designs, reflecting the reliability of study conclusions for decisions made in health practice and policy.



We describe four levels of a qualitative hierarchy of evidence-for-practice. The least likely studies to produce good evidence-for-practice are single case studies, followed by descriptive studies that may provide helpful lists of quotations but do not offer detailed analysis. More weight is given to conceptual studies that analyze all data according to conceptual themes but may be limited by a lack of diversity in the sample. Generalizable studies using conceptual frameworks to derive an appropriately diversified sample with analysis accounting for all data are considered to provide the best evidence-for-practice. Explicit criteria and illustrative examples are described for each level.



A hierarchy of evidence-for-practice specific to qualitative methods provides a useful guide for the critical appraisal of papers using these methods and for defining the strength of evidence as a basis for decision making and policy generation.
First aid skill retention of first responders within the workplace

Recent literature states that many necessary skills of CPR and first aid are forgotten shortly after certification. The purpose of this study was to determine the skill and knowledge decay in first aid in those who are paid to respond to emergency situations within a workplace.



Using a choking victim scenario, the sequence and accuracy of events were observed and recorded in 257 participants paid to act as first responders in large industrial or service industry settings. A multiple choice exam was also written to determine knowledge retention.



First aid knowledge was higher in those who were trained at a higher level, and did not significantly decline over time. Those who had renewed their certificate one or more times performed better than those who had learned the information only once. During the choking scenario many skills were performed poorly, regardless of days since last training, such as hand placement and abdominal thrusts. Compressions following the victim becoming unconscious also showed classic signs of skill deterioration after 30 days.



As many skills deteriorate rapidly over the course of the first 90 days, changing frequency of certification is not necessarily the most obvious choice to increase retention of skill and knowledge. Alternatively, methods of regularly "refreshing" a skill should be explored that could be delivered at a high frequency - such as every 90 days.
Radial gradient-index eyepiece design.

Three- and four-element eyepiece designs are presented each with a different type of radial gradient-index distribution. Both quadratic and modified quadratic index profiles are shown to provide effective control of the field aberrations. In particular, the three-element design with a quadratic index profile demonstrates that the inhomogeneous power contribution can make significant contributions to the overall system performance, especially the astigmatism correction. Using gradient-index components has allowed for increased eye relief and field of view making these designs comparable with five- and six-element ones.
Understanding and Mitigating the Security Risks of Voice-Controlled Third-Party Skills on Amazon Alexa and Google Home

Virtual personal assistants (VPA) (e.g., Amazon Alexa and Google Assistant) today mostly rely on the voice channel to communicate with their users, which however is known to be vulnerable, lacking proper authentication. The rapid growth of VPA skill markets opens a new attack avenue, potentially allowing a remote adversary to publish attack skills to attack a large number of VPA users through popular IoT devices such as Amazon Echo and Google Home. In this paper, we report a study that concludes such remote, large-scale attacks are indeed realistic. More specifically, we implemented two new attacks: voice squatting in which the adversary exploits the way a skill is invoked (e.g., “open capital one”), using a malicious skill with similarly pronounced name (e.g., “capital won”) or paraphrased name (e.g., “capital one please”) to hijack the voice command meant for a different skill, and voice masquerading in which a malicious skill impersonates the VPA service or a legitimate skill to steal the user’s data or eavesdrop on her conversations. These attacks aim at the way VPAs work or the user’s misconceptions about their functionalities, and are found to pose a realistic threat by our experiments (including user studies and real-world deployments) on Amazon Echo and Google Home. The significance of our findings have already been acknowledged by Amazon and Google, and further evidenced by the risky skills discovered on Alexa and Google markets by the new detection systems we built. We further developed techniques for automatic detection of these attacks, which already capture real-world skills likely to pose such threats. ∗All the squatting and impersonation vulnerabilities we discovered are reported to Amazon and Google and received their acknowledgement [7].
Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles

Reviews of products or services on Internet marketplace websites contain a rich amount of information. Users often wish to survey reviews or review snippets from the perspective of a certain aspect, which has resulted in a large body of work on aspect identification and extraction from such corpora. In this work, we evaluate a newly-proposed neural model for aspect extraction on two practical tasks. The first is to extract canonical sentences of various aspects from reviews, and is judged by human evaluators against alternatives. A kmeans baseline does remarkably well in this setting. The second experiment focuses on the suitability of the recovered aspect distributions to represent users by the reviews they have written. Through a set of review reranking experiments, we find that aspect-based profiles can largely capture notions of user preferences, by showing that divergent users generate markedly different review rankings.
Low complexity, low power, 10 GHz super-regenerative transceiver

In this paper a 10 GHz quasi-Hybrid/MMIC super-regenerative transceiver/antenna chip is presented. The circuit is the highest frequency super-regenerative transceiver presented in the literature and is amongst the lowest power - certainly the lowest power at X-band frequency. The chip is fabricated on GaAs substrate and uses a MMIC process for the passive components and an RFMD PHEMT chip device bonded into the circuit for the active components. The transceiver chip measures 10 × 10 mm and consumes 0.75 mW Tx and 0.9 mW Rx. When mounted into a pcb carrier substrate containing antenna, bias circuitry and low pass filtering the board measures 26 × 42 mm and operates over a range of 1 m.
Secure Computation from Random Error Correcting Codes

Secure computation consists of protocols for secure arithmetic: secret values are added and multiplied securely by networked processors. The striking feature of secure computation is that security is maintained even in the presence of an adversary who corrupts a quorum of the processors and who exercises full, malicious control over them. One of the fundamental primitives at the heart of secure computation is secret-sharing. Typically, the required secret-sharing techniques build on Shamir’s scheme, which can be viewed as a cryptographic twist on the Reed-Solomon error correcting code. In this work we further the connections between secure computation and error correcting codes. We demonstrate that threshold secure computation in the secure channels model can be based on arbitrary codes. For a network of size n, we then show a reduction in communication for secure computation amounting to a multiplicative polylogarithmic factor (in n) compared to classical methods for small, e.g., constant size fields, while tolerating t < ( 1 2 − )n players to be corrupted, where > 0 can be arbitrarily small. For large networks this implies considerable savings in communication. Our results hold in the broadcast/negligible error model of Rabin and Ben-Or, and complement results from CRYPTO 2006 for the zero-error model of Ben-Or, Goldwasser and Wigderson (BGW). Our general theory can be extended so as to encompass those results from CRYPTO 2006 as well. We also present a new method for constructing high information rate ramp schemes based on arbitrary codes, and in particular we give a new construction based on algebraic geometry codes. 1 Hao Chen’s research has been supported by NSFC grants 10225106 and 90607005. 2 Ronald Cramer’s research has been partially supported by NWO VICI. 4 Robbert de Haan’s research has been partially funded by the Dutch BSIK/BRICKS project PDC1.
A novel similarity measure for time series data with applications to gait and activity recognition

In this abstract, we propose a novel approach to modeling time-series for the purpose of comparing segments of data in order to classify activities based on accelerometer sensor data. Our approach consists of producing an ensemble of simple classifiers that can be built and can classify new data efficiently. We present empirical results from an implementation of our algorithm running on a mobile phone, demonstrating the efficiency and performance of our technique on real-world data. Our algorithm is able to identify individuals based on their gait, and can be used in a semi-supervised setting to label large data sets using a small number of labeled examples. Our method can also be used in an unsupervised setting to visualize time-series data, for example, to identify the number of different activities that occur in an unlabeled data set.
Stein Unbiased GrAdient estimator of the Risk (SUGAR) for Multiple Parameter Selection

Algorithms to solve variational regularization of ill-posed inverse problems usually involve operators that depend on a collection of continuous parameters. When these operators enjoy some (local) regularity, these parameters can be selected using the socalled Stein Unbiased Risk Estimate (SURE). While this selection is usually performed by exhaustive search, we address in this work the problem of using the SURE to efficiently optimize for a collection of continuous parameters of the model. When considering non-smooth regularizers, such as the popular l1-norm corresponding to soft-thresholding mapping, the SURE is a discontinuous function of the parameters preventing the use of gradient descent optimization techniques. Instead, we focus on an approximation of the SURE based on finite differences as proposed in [51]. Under mild assumptions on the estimation mapping, we show that this approximation is a weakly differentiable function of the parameters and its weak gradient, coined the Stein Unbiased GrAdient estimator of the Risk (SUGAR), provides an asymptotically (with respect to the data dimension) unbiased estimate of the gradient of the risk. Moreover, in the particular case of softthresholding, it is proved to be also a consistent estimator. This gradient estimate can then be used as a basis to perform a quasi-Newton optimization. The computation of the SUGAR relies on the closed-form (weak) differentiation of the non-smooth function. We provide its expression for a large class of iterative methods including proximal splitting ones and apply our strategy to regularizations involving non-smooth convex structured penalties. Illustrations on various image restoration and matrix completion problems are given.
A Particle-Based Solution for Modeling and Tracking Dynamic Digital Elevation Maps

Digital elevation maps are simple yet powerful representations of complex 3-D environments. These maps can be built and updated using various sensors and sensorial data processing algorithms. This paper describes a novel approach for modeling the dynamic 3-D driving environment, the particle-based dynamic elevation map, each cell in this map having, in addition to height, a probability distribution of speed in order to correctly describe moving obstacles. The dynamic elevation map is represented by a population of particles, each particle having a position, a height, and a speed. Particles move from one cell to another based on their speed vectors, and they are created, multiplied, or destroyed using an importance resampling mechanism. The importance resampling mechanism is driven by the measurement data provided by a stereovision sensor. The proposed model is highly descriptive for the driving environment, as it can easily provide an estimation of the height, speed, and occupancy of each cell in the grid. The system was proven robust and accurate in real driving scenarios, by comparison with ground truth data.
Security Requirements Analysis of ADS-B Networks

Due to their many advantages over their hardwarebased counterparts, Software Defined Radios are becoming the new paradigm for radio and radar applications. In particular, Automatic Dependent Surveillance-Broadcast (ADS-B) is an emerging software defined radar technology, which has been already deployed in Europe and Australia. Deployment in the US is underway as part of the Next Generation Transportation Systems (NextGen). In spite of its several benefits, this technology has been widely criticized for being designed without security in mind, making it vulnerable to numerous attacks. Most approaches addressing this issue fail to adopt a holistic viewpoint, focusing only on part of the problem. In this paper, we propose a methodology that uses semantic technologies to address the security requirements definition from a systemic perspective. More specifically, knowledge engineering focused on misuse scenarios is applied for building customized resilient software defined radar applications, as well as classifying cyber attack severity according to measurable security metrics. We showcase our ideas using an ADS-B-related scenario developed to evaluate
Exploring the impact of transformational leadership on nurse innovation behaviour: a cross-sectional study.

This study explored the influences of transformational leadership on nurse innovation behaviour and the mediating role of organisational climate.



Recently, global nursing experts have been aggressively encouraging nurses to pursue innovation in nursing in order to improve nursing outcomes. Nursing innovation, in turn, is affected by nursing leadership.



We employed a questionnaire survey to collect data, and selected a sample of nurses from hospitals in Taiwan. A total of 439 valid surveys were obtained. Hierarchical multiple regression model analysis was conducted to test the study hypothesis.



The mean values of agreement of nurse innovation behaviour and transformational leadership were 3.40 and 3.78, respectively. Patient safety climate and innovation climate were found to have full mediating effects on the relationship between transformational leadership and innovation behaviour.



Organisational climate has a significant impact on innovation behaviour. Transformational leadership has indirect effects on innovation behaviour via the mediation of patient safety climate and innovation climate.



Hospitals should enhance transformational leadership by designing leadership training programmes and establishing transformational culture. In addition, nursing managers should foster nursing innovation through improvements in organisational climate.
Body-IMU autocalibration for inertial hip and knee joint tracking

Sensor to body calibration is a key requirement for capturing accurate body movements in applications based on wearable systems. In this paper, we consider the specific problem of estimating the positions of multiple inertial measurement units (IMUs) relative to the adjacent body joints. To derive an efficient, robust and precise method based on a practical procedure is a crucial as well as challenging task when developing a wearable system with multiple embedded IMUs. In this work, first, we perform a theoretical analysis of an existing position calibration method, showing its limited applicability for the hip and knee joint. Based on this, we propose a method for simultaneously estimating the positions of three IMUs (mounted on pelvis, upper leg, lower leg) relative to these joints. The latter are here considered as an ensemble. Finally, we perform an experimental evaluation based on simulated and real data, showing the improvements of our calibration method as well as lines of future work.
Visualizing and Exploring Scientific Literature with CiteSpace: An Introduction

This half-day tutorial aims to introduce the fundamental concepts, principles and methods of visualizing and exploring the development of a scientific knowledge domain. The tutorial explains the design rationale and various applications of CiteSpace ' a freely available tool for interactive and exploratory analysis of the evolution of a scientific domain, ranging from a single specialty to multiple interrelated scientific frontiers. The tutorial demonstrates the analytic procedure of applying CiteSpace to a diverse range of examples and how one may interpret various patterns and trends revealed by interactive visual analytics.magnetic field, applied along the easy axis of the elements.