nils-herrmann/clean_scirepeval huggingface dataset:
OpenHAR: A Matlab Toolbox for Easy Access to Publicly Open Human Activity Data Sets

This study introduces OpenHAR, a free Matlab toolbox to combine and unify publicly open data sets. It provides an easy access to accelerometer signals of ten publicly open human activity data sets. Data sets are easy to access as OpenHAR provides all the data sets in the same format. In addition, units, measurement range and labels are unified, as well as, body position IDs. Moreover, data sets with different sampling rates are unified using downsampling. What is more, data sets have been visually inspected to find visible errors, such as sensor in wrong orientation. OpenHAR improves re-usability of data sets by fixing these errors. Altogether OpenHAR contains over 65 million labeled data samples. This is equivalent to over 280 hours of data from 3D accelerometers. This includes data from 211 study subjects performing 17 daily human activities and wearing sensors in 14 different body positions.
A Comparison Between the Silhouette Index and the Davies-Bouldin Index in Labelling IDS Clusters

One of the most difficult problems in the design of an anomaly b sed intrusion detection system (IDS) that uses clustering is th at of labelling the obtained clusters, i.e. determining which of them correspond t ”good” behaviour on the network/host and which to ”bad” behaviour. In this pap er, a new clusters’ labelling strategy, which makes use of a clustering quality index is proposed for application in such an IDS. The aim of the new labelling algor ithm is to detect compact clusters containing very similar vectors and these are highly likely to be attack vectors. Two clustering quality indexes have been te sted and compared: the Silhouette index and the Davies-Bouldin index. Experiment al results comparing the effectiveness of a multiple classifier IDS with the two in dexes implemented show that the system using the Silhouette index produces sli ghtly more accurate results than the system that uses the Davies-Bouldin index. However, the computation of the Davies-Bouldin index is much less complex th an the computation of the Silhouette index, which is a very important advantage regarding eventual real-time operation of an IDS that employs clustering.
Enterprise systems implementation and accounting benefits

The aim of this paper is to examine the accounting benefits involved in adopting enterprise systems, but also to comprehend the underlying causes that motivate their adoption.  This paper presents evidence from a survey of 73 companies which implemented ES on the related benefits to their accounting information provision and practices.  Empirical evidence confirms a number of benefits derived from the application of ES that focus on the following dimensions: organisation, operations, management and IT infrastructure. These benefits are related both to the reasons leading to the implementation of ES and to the relevant selection of ES modules.  The corresponding results from this survey can then be used as a basis for establishing the best approach in order to fully exploit the potential of future ES applications in accounting practice.
Open Information Extraction on Scientific Text: An Evaluation

Open Information Extraction (OIE) is the task of the unsupervised creation of structured information from text. OIE is often used as a starting point for a number of downstream tasks including knowledge base construction, relation extraction, and question answering. While OIE methods are targeted at being domain independent, they have been evaluated primarily on newspaper, encyclopedic or general web text. In this article, we evaluate the performance of OIE on scientific texts originating from 10 different disciplines. To do so, we use two state-of-the-art OIE systems using a crowd-sourcing approach. We find that OIE systems perform significantly worse on scientific text than encyclopedic text. We also provide an error analysis and suggest areas of work to reduce errors. Our corpus of sentences and judgments are made available.
Convolutional Neural Networks and Language Embeddings for End-to-End Dialect Recognition

Fusion between end-to-end system and language embeddings shows better efficiency than between end-to-end system such as MFCC and FBANK • Spectrograms achieve slightly better results than MFCCs  One of the challenges of processing real-world spoken content, such as media broadcasts, is the potential presence of different dialects of a language in the material. • Dialect identification (DID) can be a useful capability to identify which dialect is being spoken during a recording. • The Arabic Multi-Genre Broadcast (MGB) Challenge tasks have provided a valuable resource for researchers interested in processing multi-dialectal Arabic speech. • Investigation of end-to-end DID approach with dataset augmentation for acoustic feature and language embeddings for linguistic feature
DeepBE: Learning Deep Binary Encoding for Multi-label Classification

The track 2 and track 3 of ChaLearn 2016 can be considered as Multi-Label Classification problems. We present a framework of learning deep binary encoding (DeepBE) to deal with multi-label problems by transforming multi-labels to single labels. The transformation of DeepBE is in a hidden pattern, which can be well addressed by deep convolutions neural networks (CNNs). Furthermore, we adopt an ensemble strategy to enhance the learning robustness. This strategy is inspired by its effectiveness in fine-grained image recognition (FGIR) problem, while most of face related tasks such as track 2 and track 3 are also FGIR problems. By DeepBE, we got 5.45% and 10.84% mean square error for track 2 and track 3 respectively. Additionally, we proposed an algorithm adaption method to treat the multiple labels of track 2 directly and got 6.84% mean square error.
Transformational leadership in nursing: a concept analysis.

To analyse the concept of transformational leadership in the nursing context.



Tasked with improving patient outcomes while decreasing the cost of care provision, nurses need strategies for implementing reform in health care and one promising strategy is transformational leadership. Exploration and greater understanding of transformational leadership and the potential it holds is integral to performance improvement and patient safety.



Concept analysis using Walker and Avant's (2005) concept analysis method.



PubMed, CINAHL and PsychINFO.



This report draws on extant literature on transformational leadership, management, and nursing to effectively analyze the concept of transformational leadership in the nursing context.



This report proposes a new operational definition for transformational leadership and identifies model cases and defining attributes that are specific to the nursing context. The influence of transformational leadership on organizational culture and patient outcomes is evident. Of particular interest is the finding that transformational leadership can be defined as a set of teachable competencies. However, the mechanism by which transformational leadership influences patient outcomes remains unclear.



Transformational leadership in nursing has been associated with high-performing teams and improved patient care, but rarely has it been considered as a set of competencies that can be taught. Also, further research is warranted to strengthen empirical referents; this can be done by improving the operational definition, reducing ambiguity in key constructs and exploring the specific mechanisms by which transformational leadership influences healthcare outcomes to validate subscale measures.
Partial Image Encryption based on Block wise Shuffling using Arnold Map

To prevent image from unauthorized access, Encryption techniques of digital images play a very important role . The Fully image encryption techniques is very complex in nature and takes a lot of computational time. But, certain applications do not require total encryption; it requires a part of the multimedia data to be transparent to all users, like Pay-TV or Payable Internet Imaging Albums which involves encrypting the most meaningful parts of an image. In this paper we focus on a partial image encryption technique based on block wise shuffling with the help of Arnold map . Firstly image is divided into blocks then Blocks within the image are permuted by using Arnold map and final permuted block are combined that yields partial encrypted images . This process is repeated for different block size. The PSNR and NPCR obtained by our technique shows that the proposed technique gives better result than the existing techniques.
A Multiband Antenna for Satellite Communications on the Move

The design of a multiband reflector antenna for an on-the-move satellite communications terminal is presented. This antenna was designed to operate with numerous modern and future military communications satellites, which in turn requires the antenna to be capable of operation at multiple frequencies and polarizations while maintaining high aperture efficiency. Several feed antenna concepts were developed to accomplish this task, and are discussed in detail. Multiple working prototypes based on this design have been realized, with excellent performance. Measured data of individual antenna components and the complete assembly is also included
Short-term memory, working memory, and inhibitory control in children with difficulties in arithmetic problem solving.

The relations between short-term memory, working memory, inhibitory control, and arithmetic word problem solution were studied in children who were poor in arithmetic problem solving (n = 23). The children were compared with a group of good problem solvers (n = 26), matched for vocabulary, age, and gender. The results corroborate the hypothesis of poor problem solvers' general deficit in inhibitory processes. They had lower scores and made more intrusion errors in a series of working memory tasks requiring inhibition of irrelevant information. The results showed that problem solving performance is related to the ability of reducing the accessibility of nontarget and irrelevant information in memory. Span tasks that imply passive storage of information showed that poor problem solvers were impaired when they have to retain numerical information, but they did not differ from children who did not have difficulty with mathematics when the material included words.
Kirigami skins make a simple soft actuator crawl

Bioinspired soft machines made of highly deformable materials are enabling a variety of innovative applications, yet their locomotion typically requires several actuators that are independently activated. We harnessed kirigami principles to significantly enhance the crawling capability of a soft actuator. We designed highly stretchable kirigami surfaces in which mechanical instabilities induce a transformation from flat sheets to 3D-textured surfaces akin to the scaled skin of snakes. First, we showed that this transformation was accompanied by a dramatic change in the frictional properties of the surfaces. Then, we demonstrated that, when wrapped around an extending soft actuator, the buckling-induced directional frictional properties of these surfaces enabled the system to efficiently crawl.
On the de-facto Standard of Event-driven Process Chains: How EPC is defined in Literature

The Business Process Modelling Notation (BPMN) and the Event-driven Process Chain (EPC) are both frequently used modelling languages to create business process models. While there is a well-defined standard for BPMN, such a standard is missing for EPC. As a standard would be beneficial to improve interoperability among different vendors, this paper aims at providing the means for future EPC standardization. Therefore, we have conducted a structured literature review of the most common EPC variants in IS research. We provide a structured overview of the evolution of different EPC variants, describe means and capabilities and elaborate different criteria for decision-making in regard to including EPC variants in a standardization process.
Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms

Find the secret to improve the quality of life by reading this evolutionary algorithms in theory and practice evolution strategies evolutionary programming genetic algorithms. This is a kind of book that you need now. Besides, it can be your favorite book to read after having this book. Do you ask why? Well, this is a book that has different characteristic with others. You may not need to know who the author is, how well-known the work is. As wise word, never judge the words from who speaks, but make the words as your good value to your life.
An optimized system to solve text-based CAPTCHA

CAPTCHA(Completely Automated Public Turing test to Tell Computers and Humans Apart) can be used to protect data from auto bots. Countless kinds of CAPTCHAs are thus designed, while we most frequently utilize text-based scheme because of most convenience and user-friendly way . Currently, various types of CAPTCHAs need corresponding segmentation to identify single character due to the numerous different segmentation ways. Our goal is to defeat the CAPTCHA, thus firstly the CAPTCHAs need to be split into character by character. There isn’t a regular segmentation algorithm to obtain the divided characters in all kinds of examples, which means that we have to treat the segmentation individually. In this paper, we build a whole system to defeat the CAPTCHAs as well as achieve state-of-the-art performance. In detail, we present our self-adaptive algorithm to segment different kinds of characters optimally, and then utilize both the existing methods and our own constructed convolutional neural network as an extra classifier. Results are provided showing how our system work well towards defeating these CAPTCHAs.
Detecting Irony on Greek Political Tweets: A Text Mining Approach

The present work describes the classification schema for irony detection in Greek political tweets. The proposed approach relies on limited labeled training data, and its performance on a larger unlabeled dataset is evaluated qualitatively (implicitly) via a correlation study between the irony that a party receives on Twitter, its respective actual election results during the Greek parliamentary elections of May 2012, and the difference between these results and the ones of the preceding elections of 2009. The machine learning results on the labeled dataset were highly encouraging and uncovered a trend whereby the volume of ironic tweets can predict the fluctuation from previous elections.
Modeling and Characterization of Extensible Wearable Textile-Based Electrogoniometers

Measuring and monitoring through wearable technology parameters related to human movement, posture, and gesture are gaining momentum because of their wide range of potential applications in daily-life conditions. In previous studies, carbon elastomers (CEs) have been used as strain sensors. Recent developments of CE sensors mathematical modeling demonstrated that the CEs can be used as electrogoniometers. It was proved that for small local curvatures of CE layers, the resistance of a strip constituting a layer depends only on the total curvature of the same layer and not on the particular shape that the sensor keeps in adherence with a surface. Further, it was proved, theoretically and experimentally, that a double-layer configuration provides better accuracy with respect to a single-layer configuration. These results have been obtained under the hypothesis that the device was bent, but not extended. In this paper, we substituted the inextensible insulating layer in the sensors with an elastic one, allowing the system to extend its length. This improvement required further study to make it fit for biomechanical applications following epithelial deformations produced by joint movements and minimizes skin motion artifacts.
Evaluating CNN and LSTM for Web Attack Detection

Web attack detection is the key task for network security. To tackle this hard problem, this paper explores the deep learning methods, and evaluates convolutional neural network, long-short term memory and their combination method. By comparing with the traditional methods, experimental results show that deep learning methods can greatly outperform the traditional methods. Besides, we also analyze the different factors influencing the performance. This work can be a good guidance for researcher to design network architecture and parameters for detecting web attack in practice.
Design of a Compact GPS and SDARS Integrated Antenna for Automotive Applications

This letter describes a novel compact solution for integrating a Global Positioning System (GPS) and a Satellite Digital Audio Radio Service (SDARS) antenna in a very small volume to satisfy the requirements of the automotive market. The GPS antenna is a classic small commercial ceramic patch, and the SDARS antenna is designed in order to fit in the reduced space without affecting radiation and band performance of the GPS antenna. The SDARS basic geometry is a square-ring microstrip antenna operating in the frequency range 2.320-2.345 GHz with left-hand circular polarization (LHCP), and it is wrapped around the GPS patch, which receives a right-hand circular polarization (RHCP) signal at 1.575 GHz. The overall volume of the presented integrated antenna solution is only 30 x 30 x 7.6 mm3, rendering it attractive for the automotive market.
Cognitive dysfunction in psychiatric disorders: characteristics, causes and the quest for improved therapy

Studies of psychiatric disorders have traditionally focused on emotional symptoms such as depression, anxiety and hallucinations. However, poorly controlled cognitive deficits are equally prominent and severely compromise quality of life, including social and professional integration. Consequently, intensive efforts are being made to characterize the cellular and cerebral circuits underpinning cognitive function, define the nature and causes of cognitive impairment in psychiatric disorders and identify more effective treatments. Successful development will depend on rigorous validation in animal models as well as in patients, including measures of real-world cognitive functioning. This article critically discusses these issues, highlighting the challenges and opportunities for improving cognition in individuals suffering from psychiatric disorders.
Multiagent Path Finding With Persistence Conflicts

Multiagent path finding is the problem of finding paths for a set of agents—one for each agent—in a graph that the agents navigate simultaneously. Agents must navigate from their individual start to goal vertices without any collision. We argue that the prevalent treatment of path conflicts in the literature is incomplete for applications, such as computer games and crowd simulations, and extend the definition of path conflicts to accommodate cases where agents persist in their intermediate locations and even after reaching their goals. We show that an existing algorithm, conflict-based search (CBS), can be extended to handle these cases while preserving its optimality. Experiments show that our variant of CBS generates fewer nodes and runs faster than a competing algorithm.
Twitter Sentiment Analysis: A Bootstrap Ensemble Framework

Twitter sentiment analysis has become widely popular. However, stable Twitter sentiment classification performance remains elusive due to several issues: heavy class imbalance in a multi-class problem, representational richness issues for sentiment cues, and the use of diverse colloquial linguistic patterns. These issues are problematic since many forms of social media analytics rely on accurate underlying Twitter sentiments. Accordingly, a text analytics framework is proposed for Twitter sentiment analysis. The framework uses an elaborate bootstrapping ensemble to quell class imbalance, sparsity, and representational richness issues. Experiment results reveal that the proposed approach is more accurate and balanced in its predictions across sentiment classes, as compared to various comparison tools and algorithms. Consequently, the bootstrapping ensemble framework is able to build sentiment time series that are better able to reflect events eliciting strong positive and negative sentiments from users. Considering the importance of Twitter as one of the premiere social media platforms, the results have important implications for social media analytics and social intelligence.
Forward and inverse kinematic based full body gait generation of biped robot

Two legged robots have better mobility when compared with wheeled robots, but they tend to tip over easily. This will become a serious problem when the degrees of freedom (DOF) of the two legged walking machine increases. To overcome this problem the gaits of the biped robot need to be planned properly. In the present paper, a closed form solution for whole body motion of a 18-DOF biped robot, based on inverse kinematics has been proposed. The gaits for lower limbs (that is, legs) and upper limbs (that is, hands) are generated based on swing foot and hand trajectories, respectively. Further, the swing foot and hand trajectories are assumed to follow cubic polynomial. The dynamic balance of the generated full body motion of the biped robot is determined by using the concept of zero moment point (ZMP).
DB2 Design Advisor: Integrated Automatic Physical Database Design

The DB2 Design Advisor in IBM® DB2® Universal DatabaseTM (DB2 UDB) Version 8.2 for Linux®, UNIX® and Windows® is a tool that, for a given workload, automatically recommends physical design features that are any subset of indexes, materialized query tables (also called materialized views), shared-nothing database partitionings, and multidimensional clustering of tables. Our work is the very first industrial-strength tool that covers the design of as many as four different features, a significant advance to existing tools, which support no more than just indexes and materialized views. Building such a tool is challenging, because of not only the large search space introduced by the interactions among features, but also the extensibility needed by the tool to support additional features in the future. We adopt a novel “hybrid” approach in the Design Advisor that allows us to take important interdependencies into account as well as to encapsulate design features as separate components to lower the reengineering cost. The Design Advisor also features a built-in module that automatically reduces the given workload, and therefore provides great scalability for the tool. Our experimental results demonstrate that our tool can quickly provide good physical design recommendations that satisfy users’ requirements.
Through a Gender Lens: An Empirical Study of Emoji Usage over Large-Scale Android Users

Emojis have gained incredible popularity in recent years and become a new ubiquitous language for Computer-Mediated Communication (CMC) by worldwide users. Various research efforts have been made to understand the behaviors of using emojis. Gender-specific study is always meaningful for HCI community, however, so far we know very little about whether and how much males and females vary in emoji usage. To bridge such a knowledge gap, this paper makes the first effort to explore the emoji usage through a gender lens. Our analysis is based on the largest data set to date, which covers 134,419 users from 183 countries, along with their over 401 million messages collected in three months. We conduct a multi-dimensional statistical analysis from various aspects of emoji usage, including the frequency, preferences, input patterns, public/private CMC-scenario patterns, temporal patterns, and sentiment patterns. The results demonstrate that emoji usage can significantly vary between males and females. Accordingly, we propose some implications that can raise useful insights to HCI community.