nils-herrmann/clean_scirepeval huggingface dataset:
The Impact of Business Intelligence on Corporate Performance Management
Venue: 2016 49th Hawaii International Conference on System Sciences (HICSS)

While many studies focus on the role of Business Intelligence (BI) in organisations, few speak directly to the linkage between BI and Corporate Performance Management (CPM) This study aimed to address this gap by conducting an in field study among BI and CPM specialists derived from a thorough literature review and statistical analysis of the results. The study identified four BI and three CPM constructs that were then examined in greater detail. The findings suggest that Data Quality, Provision and Analysis capabilities impact on end to end organisational processes, that Technical Data and Method Integration in an organisation impacts on Organisational Alignment, and that the ability to conduct Extended Collaborative and Analytical Functions impact on the organisation's efficiency and effectiveness of CPM processes. This thus contributes to the clarification of the complex relationship between BI and CPM.
Using Data Mining Techniques for Information System Research Purposes An Examplary Application in the Field of Business Intelligence and Corporate Performance Management Research

Corporate Performance Management (CPM) is a management concept based on performance measures. These measures are supplied by Business Intelligence (BI) which transformed information technology in companies from data storage solutions towards decision support systems. It is believed that BI enhances CPM and that BI needs CPM for a purposeful commitment. To gain a detailed insight in the relationship between these two constructs a Data Mining approach is used. Data Mining is a data driven statistical approach for knowledge discovery. In comparison to commonly used Information System research approaches, like Structural Equation Modelling, in Data Mining no hypothesis have to be developed beforehand. Therefore, otherwise undiscovered patterns, information and hypothesis embedded in a given dataset can be discovered. As an example, Association Rule Discovery has been applied to a questionnaire based dataset investigating the relationship between BI and CPM. The results of the Data Mining approach show indeed more detailed information about the connection of BI and CPM than the usually applied research methods Exploratory Factor Analysis and Structural Equation Modelling.  Data Mining; Association Rule Discovery; Business Intelligence; Corporate Performance Management.
Role Conflicts And Gender Dynamics In The Family Business

There is little academic research about how societally constructed gender norms and occupational and professional norms shape gender dynamics in a family business. This results in insufficient knowledge about potential conflicts or synergies individuals in a family firm face between their familial and professional roles. This project seeks to understand how role dynamics affect decision making, managerial styles, and allocation of power and positions in the family business, and how these impact and are impacted by family dynamics. We focus on causes and effects of professional versus familial roles and how these may be gendered within family firms. This research project explores the gap in the literature by investigating the lived experiences of women and men in family businesses. The research methodology is to conduct semi structured interviews of family business owners, managers, employees and family members. The purpose of this study is to increase our understanding of the role dynamics among family members in the family business, including the intersection of the public or business side and the private family side. We are interested in exploring how the experiences of women might be different from the experiences of men in their roles as both family members and as business executives, employees or owners. Our findings should illuminate how gender and professional roles intersect in family businesses.
Average Polarization of Electromagnetic Gaussian Schell Model Beams through Anisotropic Non Kolmogorov Turbulence

Polarization properties of electromagnetic Gaussian Schell model beams propagating through the anisotropic non Kolmogorov turbulence of marine atmosphere channel are studied based on the cross spectral density matrix. Detailed analysis shows that the average polarization decreases with increasing the spectral index, inner scale of turbulence and generalized refractive index structure parameter. We find the effects of anisotropic turbulence on the average polarization is less than that of the isotropic turbulence and the depolarization effect of turbulence in marine atmosphere is larger than terreneatmosphere. The electromagnetic Gaussian Schell model beam with the parameters of smaller dyy, dxx and Ax, but larger Ay will reduce the interference of turbulence.
The Posterior Limb of the Internal Capsule as the Subcortical Transitional Zone of the Anterior and Posterior Circulations: Insights from Human 7T MRI
Venue: Cerebrovascular Diseases

In patients with cerebral infarction, identifying the distribution of infarction and the relevant artery is essential for ascertaining the underlying vascular pathophysiological mechanisms and preventing subsequent stroke. However, visualization of the basal perforating arteries (BPAs) has had limited success, and simultaneous viewing of background anatomical structures has only rarely been attempted in living human brains. Our study aimed at identifying the BPAs with 7T MRI and evaluating their distribution in the subcortical structures, thereby showing the clinical significance of the technique.  Twenty healthy subjects and 1 patient with cerebral infarction involving the posterior limb of the internal capsule (ICpost) and thalamus underwent 3 dimensional fast spoiled gradient echo sequence as time of flight magnetic resonance angiography (MRA) at 7T with a submillimeter resolution. The MRA was modified to detect inflow signals from BPAs, while preserving the background anatomical signals. BPA stems and branches in the subcortical structures and their origins were identified on images, using partial maximum intensity projection in 3 dimensions.  A branch of the left posterior cerebral artery (PCA) in the patient ran through both the infarcted thalamus and ICpost and was clearly the relevant artery. In 40 intact hemispheres in healthy subjects, 571 stems and 1,421 branches of BPAs were detected in the subcortical structures. No significant differences in the numbers of stems and branches were observed between the intact hemispheres. The numbers deviated even less across subjects. The distribution analysis showed that the subcortical structures of the telencephalon, such as the caudate nucleus, anterior limb of the internal capsule, and lenticular nucleus, were predominantly supplied by BPAs from the anterior circulation. In contrast, the thalamus, belonging to the diencephalon, was mostly fed by BPAs from the posterior circulation. However, compared with other subcortical structures, the ICpost, which marks the anatomical boundary between the telencephalon and the diencephalon, was supplied by BPAs with significantly more diverse origins. These BPAs originated from the internal carotid artery (23.1% middle cerebral artery (38.5% PCA (17.3% and the posterior communicating artery (21.1%  The modified MRI method allowed the detection of the relevant BPA within the infarcted area in the stroke survivor as well as the BPAs in the subcortical structures of living human brains. Based on in vivo BPA distribution analyses, the ICpost is the transitional zone of the anterior and posterior cerebral circulations.
Nocturnal blood pressure non dipping is not associated with increased left ventricular mass index in hypertensive children without end stage renal failure
Venue: European Journal of Pediatrics

AbstractThe aim of our study was to investigate whether nocturnal blood pressure (BP) dip is associated with increased left ventricular mass index and hypertrophy in children with hypertension (HT) We retrospectively reviewed data from all children with confirmed ambulatory HT in our center and performed ambulatory blood pressure monitoring (ABPM) and echocardiography at the same time. Left ventricular hypertrophy (LVH) was defined as left ventricular mass index (LVMI) =95th centile. Non dipping phenomenon was defined as nocturnal BP dip <10 A total of 114 ABPM studies were included, the median age of children was 15.3 years (3.8 18.9) 80 children had renoparenchymal HT without end stage renal failure, 34 had primary HT, and 27 studies were done on untreated children and 87 on treated children. Non dipping phenomenon was present in 63 (55 studies (non dippers) The LVMI adjusted for age was not significantly different between non dippers and dippers (0.87 0.03 vs. 0.81 0.02, p 0.13) Left ventricular hypertrophy was not significantly higher in non dippers than in dippers (20 vs. 9 p 0.12)  Hypertensive children without end stage renal failure with non dipping phenomenon do not have increased prevalence of LVH or higher LVMI adjusted for age than hypertensive children with preserved nocturnal BP dip. Adult and pediatric hypertensive patients with end stage renal failure have often nocturnal blood pressure non dipping phenomenon. Non dipping phenomenon is in patients with end stage renal failure associated with increased prevalence of left ventricular hypertrophy. Pediatric hypertensive patients without end stage renal failure with blood pressure non dipping phenomenon do not have increased prevalence of left ventricular hypertrophy.
Time without ventilation during intubation in neonates as a patient centred measure of performance.
Venue: Resuscitation

Time without ventilation is often much longer than an intubation attempt, yet patient stability relies on effective gas exchange. We argue that in addition to existing performance criteria, intubation performance measures should include interruption to effective ventilation.  We reviewed video recorded resuscitations of 31 term and preterm newborns that included at least one intubation attempt. Time stamps were recorded at the end of mask ventilation, laryngoscope insertion and removal (laryngoscope duration) and re commencement of ventilation via mask or endotracheal tube (ETT) Intubation attempts were defined as Successful (subsequent ventilation via ETT) or Failed (ETT incorrectly placed) or Withdrawn (laryngoscope removed before ETT insertion attempt)  During intubation, total time without ventilation varied from 31 to 273s, compared to laryngoscope duration of 12 149s. Time without ventilation as Median [min max] was greater for failed attempts 64 [48 273]s, yet laryngoscope duration was shortest for failed attempts 33 [21 46]s. Time between ceasing ventilation and commencing intubation was 5 [1 46]s suggesting room for improvement during transitions within the procedure.  Time without ventilation is a more physiologically important measure of a resuscitation team's intubation expertise than laryngoscope duration. Since successful attempts took longer than failed attempts, emphasising haste during vocal cord visualisation and tube insertion may reduce success rates. Reducing the time without ventilation at either end of the procedure may be achievable with better team coordination and could be just as important to patient wellbeing as technical precision.
Nonspatial Sequence Coding in CA1 Neurons
Venue: The Journal of Neuroscience

The hippocampus is critical to the memory for sequences of events, a defining feature of episodic memory. However, the fundamental neuronal mechanisms underlying this capacity remain elusive. While considerable research indicates hippocampal neurons can represent sequences of locations, direct evidence of coding for the memory of sequential relationships among nonspatial events remains lacking. To address this important issue, we recorded neural activity in CA1 as rats performed a hippocampus dependent sequence memory task. Briefly, the task involves the presentation of repeated sequences of odors at a single port and requires rats to identify each item as "in sequence" or "out of sequence" We report that, while the animals' location and behavior remained constant, hippocampal activity differed depending on the temporal context of items in this case, whether they were presented in or out of sequence. Some neurons showed this effect across items or sequence positions (general sequence cells) while others exhibited selectivity for specific conjunctions of item and sequence position information (conjunctive sequence cells) or for specific probe types (probe specific sequence cells) We also found that the temporal context of individual trials could be accurately decoded from the activity of neuronal ensembles, that sequence coding at the single cell and ensemble level was linked to sequence memory performance, and that slow gamma oscillations (20 40 Hz) were more strongly modulated by temporal context and performance than theta oscillations (4 12 Hz) These findings provide compelling evidence that sequence coding extends beyond the domain of spatial trajectories and is thus a fundamental function of the hippocampus.  The ability to remember the order of life events depends on the hippocampus, but the underlying neural mechanisms remain poorly understood. Here we addressed this issue by recording neural activity in hippocampal region CA1 while rats performed a nonspatial sequence memory task. We found that hippocampal neurons code for the temporal context of items (whether odors were presented in the correct or incorrect sequential position) and that this activity is linked with memory performance. The discovery of this novel form of temporal coding in hippocampal neurons advances our fundamental understanding of the neurobiology of episodic memory and will serve as a foundation for our cross species, multitechnique approach aimed at elucidating the neural mechanisms underlying memory impairments in aging and dementia.
Mapping Rice Cropping Systems in Vietnam Using an NDVI Based Time Series Similarity Measurement Based on DTW Distance
Venue: Remote. Sens.

Normalized Difference Vegetation Index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) time series data has been widely used in the fields of crop and rice classification. The cloudy and rainy weather characteristics of the monsoon season greatly reduce the likelihood of obtaining high quality optical remote sensing images. In addition, the diverse crop planting system in Vietnam also hinders the comparison of NDVI among different crop stages. To address these problems, we apply a Dynamic Time Warping (DTW) distance based similarity measure approach and use the entire yearly NDVI time series to reduce the inaccuracy of classification using a single image. We first de noise the NDVI time series using S G filtering based on the TIMESAT software. Then, a standard NDVI time series base for rice growth is established based on field survey data and Google Earth sample data. NDVI time series data for each pixel are constructed and the DTW distance with the standard rice growth NDVI time series is calculated. Then, we apply thresholds to extract rice growth areas. A qualitative assessment using statistical data and a spatial assessment using sampled data from the rice cropping map reveal a high mapping accuracy at the national scale between the statistical data, with the corresponding R2 being as high as 0.809; however, the mapped rice accuracy decreased at the provincial scale due to the reduced number of rice planting areas per province. An analysis of the results indicates that the 500 m resolution MODIS data are limited in terms of mapping scattered rice parcels. The results demonstrate that the DTW based similarity measure of the NDVI time series can be effectively used to map large area rice cropping systems with diverse cultivation processes.
Sunlight like white organic light emitting diodes with inorganic/organic nanolaminate distributed Bragg reflector (DBR) anode microcavity by using atomic layer deposition

White organic light emitting diodes (WOLED) with inorganic/organic/hybrid nanolaminate distributed Bragg reflector (DBR) prepared by atomic layer deposition (ALD) has been firstly developed, and the improved structural, optical, and electrical properties have been observed. The functional layer consists of periodically alternating layers of ZrO2/Zircone (zirconium alkoxides with carbon containing backbones) films fabricated using ALD. By well adjusting the thickness and emissive dyes of the emission layer, three white light diodes were realized based on three cavity modes with different resonance wavelength and intensity. The narrowed lectroluminescent (EL) spectra from the microcavity organic light emitting diodes (MOLEDs) are of high purity, and the off resonant optical mode is highly suppressed due to the excellent optical properties of the DBR layers with high reflectivity in a wide stop band range. The CIE chromaticity coordinates of devices are tuned from (0.33, 0.34) (0.43, 0.40) to (0.57, 0.40) which are all on the planckian locus, and the color temperature is adjusted from 5783 K, 3842 K to 2245 K. This work exhibits a simple and novel approach to achieve a sunlight like WOLED by constructing the inorganic/organic nanolaminate DBR using ALD, which will be very important for healthy display and lighting.
BaitFisher: A Software Package for Multispecies Target DNA Enrichment Probe Design.
Venue: Molecular biology and evolution

Target DNA enrichment combined with high throughput sequencing technologies is a powerful approach to probing a large number of loci in genomes of interest. However, software algorithms that explicitly consider nucleotide sequence information of target loci in multiple reference species for optimizing design of target enrichment baits to be applicable across a wide range of species have not been developed. Here we present an algorithm that infers target DNA enrichment baits from multiple nucleotide sequence alignments. By applying clustering methods and the combinatorial 1 center sequence optimization to bait design, we are able to minimize the total number of baits required to efficiently probe target loci in multiple species. Consequently, more loci can be probed across species with a given number of baits. Using transcript sequences of 24 apoid wasps (Hymenoptera: Crabronidae, Sphecidae) from the 1KITE project and the gene models of Nasonia vitripennis, we inferred 57,650, 120 bp long baits for capturing 378 coding sequence sections of 282 genes in apoid wasps. Illumina reduced representation library sequencing confirmed successful enrichment of the target DNA when applying these baits to DNA of various apoid wasps. The designed baits furthermore enriched a major fraction of the target DNA in distantly related Hymenoptera, such as Formicidae and Chalcidoidea, highlighting the baits' broad taxonomic applicability. The availability of baits with broad taxonomic applicability is of major interest in numerous disciplines, ranging from phylogenetics to biodiversity monitoring. We implemented our new approach in a software package, called BaitFisher, which is open source and freely available at https:/github.com/cmayer/BaitFisher package.git.
Adversarial Feature Selection Against Evasion Attacks
Venue: IEEE Transactions on Cybernetics

Pattern recognition and machine learning techniques have been increasingly adopted in adversarial settings such as spam, intrusion, and malware detection, although their security against well crafted attacks that aim to evade detection by manipulating data at test time has not yet been thoroughly assessed. While previous work has been mainly focused on devising adversary aware classification algorithms to counter evasion attempts, only few authors have considered the impact of using reduced feature sets on classifier security against the same attacks. An interesting, preliminary result is that classifier security to evasion may be even worsened by the application of feature selection. In this paper, we provide a more detailed investigation of this aspect, shedding some light on the security properties of feature selection against evasion attacks. Inspired by previous work on adversary aware classifiers, we propose a novel adversary aware feature selection model that can improve classifier security against evasion attacks, by incorporating specific assumptions on the adversary's data manipulation strategy. We focus on an efficient, wrapper based implementation of our approach, and experimentally validate its soundness on different application examples, including spam and malware detection.
Secure Kernel Machines against Evasion Attacks
Venue: AISec@CCS

Machine learning is widely used in security sensitive settings like spam and malware detection, although it has been shown that malicious data can be carefully modified at test time to evade detection. To overcome this limitation, adversary aware learning algorithms have been developed, exploiting robust optimization and game theoretical models to incorporate knowledge of potential adversarial data manipulations into the learning algorithm. Despite these techniques have been shown to be effective in some adversarial learning tasks, their adoption in practice is hindered by different factors, including the difficulty of meeting specific theoretical requirements, the complexity of implementation, and scalability issues, in terms of computational time and space required during training. In this work, we aim to develop secure kernel machines against evasion attacks that are not computationally more demanding than their non secure counterparts. In particular, leveraging recent work on robustness and regularization, we show that the security of a linear classifier can be drastically improved by selecting a proper regularizer, depending on the kind of evasion attack, as well as unbalancing the cost of classification errors. We then discuss the security of nonlinear kernel machines, and show that a proper choice of the kernel function is crucial. We also show that unbalancing the cost of classification errors and varying some kernel parameters can further improve classifier security, yielding decision functions that better enclose the legitimate data. Our results on spam and PDF malware detection corroborate our analysis.
Strategic multiyear transmission expansion planning under severe uncertainties by a combination of melody search algorithm and Powell heuristic method

In this paper, a new strategic multiyear model is presented for Transmission Expansion Planning (TEP) in deregulated environments. This methodology is based on a tri level decision making whose fundamental elements are pool based electricity market and strategic behavior of market participants. In addition, to minimize risks of planning arising from severe uncertainties, an information gap decision theory (IGDT) is used. By using the IGDT, the TEP model is formulated for the risk averse and risk seeker decision makers through the robustness and opportunity models, respectively. The offered model is formulated as a non convex mixed integer non linear optimization problem. With this regards, a combination of melody search algorithm and improved Powell heuristic method is widely used to determine the optimal solution. The planning methodology has been applied to the IEEE 30 bus test system and to the large scale Iranian 400 kV transmission grid. Simulation results demonstrate the feasibility and effectiveness of the proposed model, and the fact that it can be profitable for the real world networks.