nils-herrmann/clean_scirepeval huggingface dataset:
Parameter extraction algorithm for one-diode model of PV panels based on datasheet values

A photovoltaic (PV) panel model is at the heart of an accurate performance model for a large PV farm. This paper presents an algorithm to calculate the parameters of the one-diode model of PV modules based solely on the manufacturer's datasheets. The important feature of this algorithm is that through reformulation of the characteristic equations at various points of the current-voltage (I-V) curve, the unknown model parameters can be determined analytically. This is in contrast to many existing models which choose a value for one parameter and then calculate the other parameters through simultaneous solution of the system of equations. The calculated I-V curve is then compared with the manufacturer's curve to validate the proposed algorithm and quantify the modeling error.
Remain seated: towards fully-immersive desktop VR

In this work we describe the scenario of fully-immersive desktop VR, which serves the overall goal to seamlessly integrate with existing workflows and workplaces of data analysts and researchers, such that they can benefit from the gain in productivity when immersed in their data-spaces. Furthermore, we provide a literature review showing the status quo of techniques and methods available for realizing this scenario under the raised restrictions. Finally, we propose a concept of an analysis framework and the decisions made and the decisions still to be taken, to outline how the described scenario and the collected methods are feasible in a real use case.
Gyroscope Technology and Applications: A Review in the Industrial Perspective

This paper is an overview of current gyroscopes and their roles based on their applications. The considered gyroscopes include mechanical gyroscopes and optical gyroscopes at macro- and micro-scale. Particularly, gyroscope technologies commercially available, such as Mechanical Gyroscopes, silicon MEMS Gyroscopes, Ring Laser Gyroscopes (RLGs) and Fiber-Optic Gyroscopes (FOGs), are discussed. The main features of these gyroscopes and their technologies are linked to their performance.
Temporal Segment Networks for Action Recognition in Videos

Deep convolutional networks have achieved great success for image recognition. However, for action recognition in videos, their advantage over traditional methods is not so evident. We present a general and flexible video-level framework for learning action models in videos. This method, called temporal segment network (TSN), aims to model long-range temporal structures with a new segment-based sampling and aggregation module. This unique design enables our TSN to efficiently learn action models by using the whole action videos. The learned models could be easily adapted for action recognition in both trimmed and untrimmed videos with simple average pooling and multi-scale temporal window integration, respectively. We also study a series of good practices for the instantiation of TSN framework given limited training samples. Our approach obtains the state-the-of-art performance on four challenging action recognition benchmarks: HMDB51 (71.0%), UCF101 (94.9%), THUMOS14 (80.1%), and ActivityNet v1.2 (89.6%). Using the proposed RGB difference for motion models, our method can still achieve competitive accuracy on UCF101 (91.0 %) while running at 340 FPS. Furthermore, based on the temporal segment networks, we won the video classification track at the ActivityNet challenge 2016 among 24 teams, which demonstrates the effectiveness of TSN and the proposed good practices.
Penetration Testing in Agile Software Development Projects

Agile development methods are commonly used to iteratively develop the information systems and they can easily handle ever-changing business requirements. Scrum is one of the most popular agile software development frameworks. The popularity is caused by the simplified process framework and its focus on teamwork. The objective of Scrum is to deliver working software and demonstrate it to the customer faster and more frequent during the software development project. However the security requirements for the developing information systems have often a low priority. This requirements prioritization issue results in the situations where the solution meets all the business requirements but it is vulnerable to potential security threats. The major benefit of the Scrum framework is the iterative development approach and the opportunity to automate penetration tests. Therefore the security vulnerabilities can be discovered and solved more often which will positively contribute to the overall information system protection against potential hackers. In this research paper the authors propose how the agile software development framework Scrum can be enriched by considering the penetration tests and related security requirements during the software development lifecycle. Authors apply in this paper the knowledge and expertise from their previous work focused on development of the new information system penetration tests methodology PETA with focus on using COBIT 4.1 as the framework for management of these tests, and on previous work focused on tailoring the project management framework PRINCE2 with Scrum. The outcomes of this paper can be used primarily by the security managers, users, developers and auditors. The security managers may benefit from the iterative software development approach and penetration tests automation. The developers and users will better understand the importance of the penetration tests and they will learn how to effectively embed the tests into the agile development lifecycle. Last but not least the auditors may use the outcomes of this paper as recommendations for companies struggling with penetrations testing embedded in the agile software development process.
Deep Unsupervised Similarity Learning Using Partially Ordered Sets

Unsupervised learning of visual similarities is of paramount importance to computer vision, particularly due to lacking training data for fine-grained similarities. Deep learning of similarities is often based on relationships between pairs or triplets of samples. Many of these relations are unreliable and mutually contradicting, implying inconsistencies when trained without supervision information that relates different tuples or triplets to each other. To overcome this problem, we use local estimates of reliable (dis-)similarities to initially group samples into compact surrogate classes and use local partial orders of samples to classes to link classes to each other. Similarity learning is then formulated as a partial ordering task with soft correspondences of all samples to classes. Adopting a strategy of self-supervision, a CNN is trained to optimally represent samples in a mutually consistent manner while updating the classes. The similarity learning and grouping procedure are integrated in a single model and optimized jointly. The proposed unsupervised approach shows competitive performance on detailed pose estimation and object classification.
Graph-based models of space in architecture and cognitive science: a comparative analysis

Graph-based operationalizations of space are used in architecture as well as in cognitive science. In such models, environments are usually described by means of nodes and edges, roughly corresponding to places and their spatial relations. In the field of cognitive science, view and place graphs are models of mental representations of environments and used for the explanation of wayfinding behavior such as exploration and route planning. In architecture, space syntax and visibility graph analysis aim at identifying and describing structural properties of built environments that determine their usage and experience. In cognitive science, mental representations of space cannot be seen independently from the formal and configurational properties of the corresponding environments that are well captured by architectural description systems. Vice versa, formal descriptions of space as used in architecture gain plausibility and relevance by incorporating results from cognitive research that allow the prediction and explanation of actual human behavior. In this paper approaches from the two different disciplines are therefore reviewed and compared. Special interest concerns their scope, structure, and representational content. Parallels, differences, and specific strengths are discussed. Furthermore, based on recent empiric work, strategies to integrate aspects from both disciplines are outlined.
Bag-of-visual-words models for adult image classification and filtering

We present a method to classify images into different categories of pornographic content to create a system for filtering pornographic images from network traffic. Although different systems for this application were presented in the past, most of these systems are based on simple skin colour features and have rather poor performance. Recent advances in the image recognition field in particular for the classification of objects have shown that bag-of-visual-words-approaches are a good method for many image classification problems. The system we present here, is based on this approach, uses a task-specific visual vocabulary and is trained and evaluated on an image database of 8500 images from different categories. It is shown that it clearly outperforms earlier systems on this dataset and further evaluation on two novel web-traffic collections shows the good performance of the proposed system.
Mining and Sentiment Analysis using Bayesian and Neural Networks Approaches

Information technologies have firmly entered our life and it is impossible to imagine our life without gadgets or the Internet. Today, social media is not only a source that broadcasts information to the users, but it allows users to intercommunicate and share their views and experience with each other. Some portion of such data is subjective and contains opinionated information that can be further analyzed to retrieve essential data from it and later use for various purposes for analysis and decision support. In order to use this type of that the first step is to understand it and categorize opinions in the information. Hence, in this dissertation, sentiment analysis techniques are studied in order to retrieve opinions from the tweets. In order to ensure efficient classification, it is important to apply algorithms that perform well on this task. Therefore, the main goal of the thesis is to investigate algorithms that can be applied for the opinion estimation. To that extend, data preprocessing and several experiments are conducted, namely, the classifier is trained and tested on two different datasets with two different classifiers (Naive Bayes and convolutional neural network). In addition, the influence of the training data on the classifier efficiency is discussed.
Challenges in Discriminating Profanity from Hate Speech

In this study we approach the problem of distinguishing general profanity from hate speech in social media, something which has not been widely considered. Using a new dataset annotated specifically for this task, we employ supervised classification along with a set of features that includes n-grams, skip-grams and clustering-based word representations. We apply approaches based on single classifiers as well as more advanced ensemble classifiers and stacked generalization, achieving the best result of 80% accuracy for this 3-class classification task. Analysis of the results reveals that discriminating hate speech and profanity is not a simple task, which may require features that capture a deeper understanding of the text not always possible with surface n-grams. The variability of gold labels in the annotated data, due to differences in the subjective adjudications of the annotators, is also an issue. Other directions for future work are discussed.
Using natural language processing technology for qualitative data analysis

Social researchers often apply qualitative research methods to study groups and their communications artefacts. The use of computer-mediated communications has dramatically increased the volume of text available, but coding such text requires considerable manual effort. We discuss how systems that process text in human languages (i.e., natural language processing, NLP) might partially automate content analysis by extracting theoretical evidence. We present a case study of the use of NLP for qualitative analysis in which the NLP rules showed good performance on a number of codes. With the current level of performance, use of an NLP system could reduce the amount of text to be examined by a human coder by an order of magnitude or more, potentially increasing the speed of coding by a comparable degree. The paper is significant as it is one of the first to demonstrate the use of high-level NLP techniques for qualitative data analysis.
Antenna Range Illuminator Based on a Septum Polarizer and a Dual-Mode Horn [Measurements Corner]

In this article, an original design of a range illuminator based on a stepped-septum polarizer and a dual-mode horn is discussed. However, the designed polarizer can also be used with other waveguide antennas, such as pyramidal horns and corrugated horns. A simple procedure for determining the initial septum geometry for the subsequent optimization is described, and based on this, a two-step septum polarizer is developed. A carefully designed dual-mode horn antenna with a rectangular aperture is then utilized to increase the illuminator's gain and suppress sidelobe levels. Results of full-wave electromagnetic simulations are presented and compared to experimentally measured data with good agreement.
Evaluations of the uptake and impact of the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) Statement and extensions: a scoping review

The PRISMA Statement is a reporting guideline designed to improve transparency of systematic reviews (SRs) and meta-analyses. Seven extensions to the PRISMA Statement have been published to address the reporting of different types or aspects of SRs, and another eight are in development. We performed a scoping review to map the research that has been conducted to evaluate the uptake and impact of the PRISMA Statement and extensions. We also synthesised studies evaluating how well SRs published after the PRISMA Statement was disseminated adhere to its recommendations.



We searched for meta-research studies indexed in MEDLINE® from inception to 31 July 2017, which investigated some component of the PRISMA Statement or extensions (e.g. SR adherence to PRISMA, journal endorsement of PRISMA). One author screened all records and classified the types of evidence available in the studies. We pooled data on SR adherence to individual PRISMA items across all SRs in the included studies and across SRs published after 2009 (the year PRISMA was disseminated).



We included 100 meta-research studies. The most common type of evidence available was data on SR adherence to the PRISMA Statement, which has been evaluated in 57 studies that have assessed 6487 SRs. The pooled results of these studies suggest that reporting of many items in the PRISMA Statement is suboptimal, even in the 2382 SRs published after 2009 (where nine items were adhered to by fewer than 67% of SRs). Few meta-research studies have evaluated the adherence of SRs to the PRISMA extensions or strategies to increase adherence to the PRISMA Statement and extensions.



Many studies have evaluated how well SRs adhere to the PRISMA Statement, and the pooled result of these suggest that reporting of many items is suboptimal. An update of the PRISMA Statement, along with a toolkit of strategies to help journals endorse and implement the updated guideline, may improve the transparency of SRs.
Auxin–Cytokinin Interaction Regulates Meristem Development

Plant hormones regulate many aspects of plant growth and development. Both auxin and cytokinin have been known for a long time to act either synergistically or antagonistically to control several significant developmental processes, such as the formation and maintenance of meristem. Over the past few years, exciting progress has been made to reveal the molecular mechanisms underlying the auxin-cytokinin action and interaction. In this review, we shall briefly discuss the major progress made in auxin and cytokinin biosynthesis, auxin transport, and auxin and cytokinin signaling. The frameworks for the complicated interaction of these two hormones in the control of shoot apical meristem and root apical meristem formation as well as their roles in in vitro organ regeneration are the major focus of this review.
Images2Poem: Generating Chinese Poetry from Image Streams

Natural language generation from visual inputs has attracted extensive research attention recently. Generating poetry from visual content is an interesting but very challenging task. We propose and address the new multimedia task of generating classical Chinese poetry from image streams. In this paper, we propose an Images2Poem model with a selection mechanism and an adaptive self-attention mechanism for the problem. The model first selects representative images to summarize the image stream. During decoding, it adaptively pays attention to the information from either source-side image stream or target-side previously generated characters. It jointly summarizes the images and generates relevant, high-quality poetry from image streams. Experimental results demonstrate the effectiveness of the proposed approach. Our model outperforms baselines in different human evaluation metrics.
Short-Term Plasticity and Synaptic Filtering Emulated in Electrolyte-Gated IGZO Transistors

Artificial synaptic devices have attracted a broad interest for hardware implementation of brain-inspired neuromorphic systems. In this letter, a short-term plasticity simulation in an indium-gallium-zinc oxide (IGZO) electric-double-layer (EDL) transistor is investigated. For synaptic facilitation and depression function emulation, three-terminal EDL transistor is reduced to a two-terminal synaptic device with two modified connection schemes. Furthermore, high-pass and low-pass filtering characteristics are also successfully emulated not only for fixed-rate spike train but also for Poisson-like spike train. Our results suggest that IGZO-based EDL transistors operated in two terminal mode can be used as the building blocks for brain-like chips and neuromorphic systems.
Categorical Probability Proportion Difference ( CPPD ) : A Feature Selection Method for Sentiment Classification

Sentiment analysis is to extract the opinion of the user from of the text documents. Sentiment classification using machine learning methods face problem of handling huge number of unique terms in a feature vector for the classification. Thus it is required to eliminate the irrelevant and noisy terms from the feature vector. Feature selection methods reduce the feature size by selecting prominent features for better classification. In this paper, a new feature selection method namely Probability Proportion Difference (PPD) is proposed which is based on the probability of belongingness of a term to a particular class. It is capable of removing irrelevant terms from the feature vector. Further, a Categorical Probability Proportion Difference (CPPD) feature selection method is proposed based on Probability Proportion Difference (PPD) and Categorical Proportion Difference (CPD). CPPD feature selection method is able to select the features which are relevant and capable of discriminating the class. The performance of the proposed feature selection methods is compared with the CPD method and Information Gain (IG) method which has been identified as one of the best feature selection method for sentiment classification. Experimentation of proposed feature selection methods was performed on two standard datasets viz. movie review dataset and product review (i.e. book) dataset. Experimental results show that proposed CPPD feature selection method outperforms other feature selection method for sentiment classification.
Road to safe autonomy with data and formal reasoning

We present an overview of recently developed data-driven tools for safety analysis of autonomous vehicles and advanced driver assist systems. The core algorithms combine model-based, hybrid system reachability analysis with sensitivity analysis of components with unknown or inaccessible models. We illustrate the applicability of this approach with a new case study of emergency braking systems in scenarios with two or three vehicles. This problem is representative of the most common type of rear-end crashes, which is relevant for safety analysis of automatic emergency braking (AEB) and forward collision avoidance systems. We show that our verification tool can effectively prove the safety of certain scenarios (specified by several parameters like braking profiles, initial velocities, uncertainties in position and reaction times), and also compute the severity of accidents for unsafe scenarios. Through hundreds of verification experiments, we quantified the safety envelope of the system across relevant parameters. These results show that the approach is promising for design, debugging and certification. We also show how the reachability analysis can be combined with statistical information about the parameters, to assess the risk-level of the control system, which in turn is essential, for example, for determining Automotive Safety Integrity Levels (ASIL) for the ISO26262 standard.
Emotional Intelligence and Transformational and Transactional Leadership : A Meta-Analysis

The purpose of this study is to evaluate claims that emotional intelligence is significantly related to transformational and other leadership behaviors.  (based on 62 independent samples) indicated a validity estimate of .59 when ratings of both emotional intelligence and leadership behaviors were provided by the same source (self, subordinates, peers, or superiors). However, when ratings of the constructs were derived from different sources, the validity estimate was .12. Lower validity estimates were found for transactional and laissez-faire leadership behaviors. Separate analyses were performed for each measure of emotional intelligence. Trait measures of emotional intelligence tended to show higher validities than ability-based measures of emotional intelligence. Agreement across ratings sources for the same construct was low for both transformational leadership (.14) and emotional intelligence (.16).
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking

In this supplementary material of [6] we provide additional derivations and implementation details. Section 1 provides details about the derivation of eq. (5) in the paper. Section 2 contains detailed derivations of the Fourier coefficients of the desired convolution output yj and the interpolation functions bd, described in section 3.4 in the paper. Details about the numerical optimization is given in section 3. We provide detailed results on the OTB-2015 and Temple-Color datasets in sections 4 and 5 respectively. In our object tracking experiments, we use the same parameter settings for our method in all state-of-the-art comparisons (sections 5.2, 5.3 and 5.4), i.e. for all datasets and videos. Further, we use the same parameter settings for all feature point tracking experiments. Code, raw result files and a video of qualitative feature point tracking results on the MPI Sintel dataset are available at the project webpage http://www.cvl.isy. liu.se/research/objrec/visualtracking/conttrack/index.html.