nils-herrmann/clean_scirepeval huggingface dataset:
Discovering and Exploring State Based Models for Multi perspective Processes
Venue: BPM

Process mining provides fact based insights into process behaviour captured in event data. In this work we aim to discover models for processes where different facets, or perspectives, of the process can be identified. Instead of focussing on the events or activities that are executed in the context of a particular process, we concentrate on the states of the different perspectives and discover how they are related. We present a formalisation of these relations and an approach to discover state based models highlighting them. The approach has been implemented using the process mining framework ProM and provides a highly interactive visualisation of the multi perspective state based models. This tool has been evaluated on the BPI Challenge 2012 data of a loan application process and on product user behaviour data gathered by Philips during the development of a smart baby bottle equipped with various sensors.
Effect of 4 nonylphenol on the immune response of the Pacific oyster Crassostrea gigas following bacterial infection with Vibrio campbellii.
Venue: Fish shellfish immunology

The xenoestrogen 4 nonylphenol (NP) is a ubiquitous aquatic pollutant and has been shown to impair reproduction, development, growth and, more recently, immune function in marine invertebrates. We investigated the effects of short term (7 d) exposure to low (2 mg l 1) and high (100 mg l 1) levels of NP on cellular and humoral elements of the innate immune response of Crassostrea gigas to a bacterial challenge. To this end, we measured 1) total hemocyte counts (THC) 2) relative transcript abundance of ten immune related genes (defh1, defh2, bigdef1, bigdef2, bpi, lysozyme 1, galectin, C type lectin 2, timp, and transglutaminase) in the hemocytes, gill and mantle, and 3) hemolymph plasma lysozyme activity, following experimental Vibrio campbellii infection. Both low and high levels of NP were found to repress a bacteria induced increase in THC observed in the control oysters. While several genes were differentially expressed following bacterial introduction (bigdef2, bpi, lysozyme 1, timp, transglutaminase) only two genes (bpi in the hemocytes, transglutaminase in the mantle) exhibited a different bacteria induced expression profile following NP exposure, relative to the control oysters. Independently of infection status, exposure to NP also altered mRNA transcript abundance of several genes (bpi, galectin, C type lectin 2) in naive, saline injected oysters. Finally, plasma lysozyme activity levels were significantly higher in low dose NP treated oysters (both naive and bacteria challenged) relative to control oysters. Combined, these results suggest that exposure to ecologically relevant (low) and extreme (high) levels of NP can alter both cellular and humoral elements of the innate immune response in C. gigas, an aquaculture species of global economic importance.
Composite State Machine Miner: Discovering and Exploring Multi perspective Processes
Venue: BPM

Process mining provides fact based insights into processes based on behaviour captured in event data. An important aspect of this is the discovery of process models from such data. Traditionally, the focus of process discovery is on learning the ordering of activities. We deviate from this dominating activity view on processes to focus on states and state changes. Specifically, we aim to discover state based models for processes where different facets, or perspectives, of the process can be identified. In this paper we describe an interactive process discovery tool that can be used to discover and explore state based models for such multi perspective processes: the Composite State Machine Miner. It quantifies and visualises the interactions between perspectives to provide additional process insights. This tool has been used to analyse the BPI Challenge 2012 data of a loan application process and product user behaviour data gathered by Philips during the development of a smart baby bottle equipped with various sensors.
Mining Reference Process Models from Large Instance Data
Venue: Business Process Management Workshops

Reference models provide generic blueprints of process models that are common in a certain industry. When designing a reference model, stakeholders have to cope with the so called 'dilemma of reference modeling' viz. balancing generality against market specificity. In principle, the more details a reference model contains, the fewer situations it applies to. To overcome this dilemma, the contribution at hand presents a novel approach to mining a reference model hierarchy from large instance level data such as execution logs. It combines an execution semantic technique for reference model development with a hierarchical agglomerative cluster analysis and ideas from Process Mining. The result is a reference model hierarchy, where the lower a model is located, the smaller its scope, and the higher its level of detail. The approach is implemented as proof of concept and applied in an extensive case study, using the data from the 2015 BPI Challenge.
Vitamin D Supplementation Affects the Beck Depression Inventory, Insulin Resistance, and Biomarkers of Oxidative Stress in Patients with Major Depressive Disorder: A Randomized, Controlled Clinical Trial.
Venue: The Journal of nutrition

Vitamin D may decrease depression symptoms through its beneficial effects on neurotransmitters, metabolic profiles, biomarkers of inflammation, and oxidative stress.  This study was designed to assess whether vitamin D supplementation can reduce symptoms of depression, metabolic profiles, serum high sensitivity C reactive protein (hs CRP) and biomarkers of oxidative stress in patients with major depressive disorder (MDD) METHODS This randomized, double blind, placebo controlled clinical trial was performed in 40 patients between 18 and 65 y of age with a diagnosis of MDD based on criteria from the Diagnostic and Statistical Manual of Mental Disorders. Patients were randomly assigned to receive either a single capsule of 50 kIU vitamin D/wk (n 20) or placebo (n 20) for 8 wk. Fasting blood samples were taken at baseline and postintervention to quantify relevant variables. The primary [Beck Depression Inventory (BDI) which examines depressive symptoms] and secondary (glucose homeostasis variables, lipid profiles, hs CRP, and biomarkers of oxidative stress) outcomes were assessed.  Baseline concentrations of mean serum 25 hydroxyvitamin D were significantly different between the 2 groups (9.2 6.0 and 13.6 7.9 mg/L in the placebo and control groups, respectively, P 0.02) After 8 wk of intervention, changes in serum 25 hydroxyvitamin D concentrations were significantly greater in the vitamin D group +20.4 mg/L) than in the placebo group 0.9 mg/L, P 0.001) A trend toward a greater decrease in the BDI was observed in the vitamin D group than in the placebo group 8.0 and 3.3, respectively, P 0.06) Changes in serum insulin 3.6 compared with +2.9 mIU/mL, P 0.02) estimated homeostasis model assessment of insulin resistance 1.0 compared with +0.6, P 0.01) estimated homeostasis model assessment of b cell function 13.9 compared with +10.3, P 0.03) plasma total antioxidant capacity +63.1 compared with 23.4 mmol/L, P 0.04) and glutathione +170 compared with 213 mmol/L, P 0.04) in the vitamin D group were significantly different from those in the placebo group.  Overall, vitamin D supplementation of patients with MDD for 8 wk had beneficial effects on the BDI, indicators of glucose homeostasis, and oxidative stress.
Feature Learning Based Deep Supervised Hashing with Pairwise Labels
Venue: IJCAI

Recent years have witnessed wide application of hashing for large scale image retrieval. However, most existing hashing methods are based on handcrafted features which might not be optimally compatible with the hashing procedure. Recently, deep hashing methods have been proposed to perform simultaneous feature learning and hash code learning with deep neural networks, which have shown better performance than traditional hashing methods with hand crafted features. Most of these deep hashing methods are supervised whose supervised information is given with triplet labels. For another common application scenario with pairwise labels, there have not existed methods for simultaneous feature learning and hash code learning. In this paper, we propose a novel deep hashing method, called deep pairwise supervised hashing (DPSH) to perform simultaneous feature learning and hashcode learning for applications with pairwise labels. Experiments on real datasets show that our DPSH method can outperform other methods to achieve the state of the art performance in image retrieval applications.
Deep Supervised Hashing with Triplet Labels
Venue: ACCV

Hashing is one of the most popular and powerful approximate nearest neighbor search techniques for large scale image retrieval. Most traditional hashing methods first represent images as off the shelf visual features and then produce hashing codes in a separate stage. However, off the shelf visual features may not be optimally compatible with the hash code learning procedure, which may result in sub optimal hash codes. Recently, deep hashing methods have been proposed to simultaneously learn image features and hash codes using deep neural networks and have shown superior performance over traditional hashing methods. Most deep hashing methods are given supervised information in the form of pairwise labels or triplet labels. The current state of the art deep hashing method DPSH  which is based on pairwise labels, performs image feature learning and hash code learning simultaneously by maximizing the likelihood of pairwise similarities. Inspired by DPSH  we propose a triplet label based deep hashing method which aims to maximize the likelihood of the given triplet labels. Experimental results show that our method outperforms all the baselines on CIFAR 10 and NUS WIDE datasets, including the state of the art method DPSH  and all the previous triplet label based deep hashing methods.
Recent trends in epidemiology, sensitization and legal requirements of selected relevant contact allergens
Venue: Expert review of clinical immunology

Numerous studies have focused on emerging allergens causing contact allergy and allergic contact dermatitis in eczema populations and the general population, whereas only a few European multicenter studies systematically manage to detect prevalence rates of various contact allergies over time in eczema populations. Contact allergy is a life time condition, which may lead to allergic contact dermatitis in individuals who do not manage to avoid exposure to the ascertained allergen in question. It is therefore of utmost importance that clinicians and dermatologists have sufficient knowledge on common allergens causing contact allergies in the general and working population. This review aimed to highlight the newest knowledge of frequent allergens of clinical importance. Literature was sought from the Pubmed(tm) database, Google(tm) scholar and textbooks. On the basis of the literature within the last 5 years, a comprehensive review of methylisothiazolinone, chromium, cobalt, rubber accelerators and fragrance ingredients were conducted. Of each allergen we discuss in detail the temporal trend of prevalence, source of exposure, clinical manifestation of allergic contact dermatitis and legislative measurements on how to regulate the exposure.
Medical Costs and Healthcare Utilization among Cancer Decedents in the Last Year of Life in 2009
Venue: Cancer research and treatment official journal of Korean Cancer Association

The purpose of this study was to evaluate the cancer care cost during the last year of life of patients in Korea.  We studied the breakdown of spending on the components of cancer care. Cancer decedents in 2009 were identified from the Korean Central Cancer Registry and linked with the Korean National Health Insurance Claims Database. The final number of patients included in the study was 70,558.  In 2009, the average cancer care cost during the last year of life was US $15,720. Patients under age 20 spent US $53,890 while those 70 or over spent US $11,801. Those with leukemia incurred the highest costs (US $43,219) while bladder cancer patients spent the least (US $13,155) General costs, drugs other than analgesics, and test fees were relatively high (29.7% 23.8% and 20.7% of total medical costs, respectively) Analgesic drugs, rehabilitation, and psychotherapy were still relatively low (4.3% 0.7% and 0.1% respectively) Among the results of multiple regression analysis, few were notable. Age was found to be negatively related to cancer care costs while income level was positively associated. Those classified under distant Surveillance, Epidemiology, and End Results stages of cancer and higher comorbidity level also incurred higher cancer care costs.  Average cancer care costs varied significantly by patient characteristics. However, the study results suggest an underutilization of support services likely due to lack of alternative accommodations for terminal cancer patients. Further examination of utilization patterns of healthcare resources will help provide tailored evidence for policymakers in efforts to reduce the burdens of cancer care.
Unobserved Heterogeneity in Hospitality and Tourism Research

Despite the growing complexity of structural equation model (SEM) applications in tourism, it is surprising that most applications have estimated these models without accounting for unobserved heterogeneity. In this article, we aim to discuss the concept of unobserved heterogeneity in more detail, highlighting its serious threats to the validity and reliability of SEMs. We describe a Bayesian finite mixture modeling framework for estimating SEMs while accounting for unobserved heterogeneity. We provide a comprehensive description of this model, and provide guidance on its estimation using the WinBUGS software. We illustrate the importance of unobserved heterogeneity and the finite mixture modeling framework using a didactic application on brand equity where heterogeneity is likely to play an important role because of the differences in how consumers perceive the different dimensions of brand equity. We compare between various models and illustrate the differences between the standard and heterogeneous SEM and discuss the implications for research and practice.
What's Really Happening Here? Effectively Using Surveys to Learn About Organizations

Scholars and practitioners of public management need reliable data as they strive to understand public organizations. This article considers various sources of data and how they can be effectively leveraged to learn about organizations and their employees. Common problems associated with archival and survey data are identified, and several recommendations regarding survey design and administration are offered, including a consideration of who (clients, managers, or front line employees) might provide the best information about various topics that may be of interest to researchers or practitioners conducting surveys. Finally, two examples of effective data collection efforts are briefly discussed.
How to learn to love your research ethics committee: recommendations for psychologists

Ethics review of psychological and sociobehavioural research is increasingly required by leading South African research institutions and universities, following international trends, and national statutory developments. Local and international scholarly journals are also more routinely requesting proof of ethics approval before accepting empirical work for publication. In some instances, psychological researchers may regard ethics review as a process that imposes delays and adds little value to proposed studies, and they may experience the process as frustrating and unrewarding. This article aims to briefly review the issue of ethics review for such research and to focus on pragmatic recommendations for psychological researchers navigating ethical review, including how they could engage their research ethics committee more effectively to strengthen this critical relationship.
Manifold denoising based on spectral graph wavelets
Venue: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

We propose a new framework for manifold denoising using the Spectral Graph Wavelet transform, which enables non iterative denoising directly in the graph frequency domain, an approach inspired by conventional wavelet based signal denoising methods. We theoretically justify our approach, based on the fact that for smooth manifolds the coordinate information tends to create energy in the low spectral graph wavelet coefficients, while the noise affects all frequency bands in a similar way. Experimental results show that our suggested manifold frequency denoising (MFD) approach significantly outperforms the state of the art manifold denosing methods, and is robust to a wide range of parameter selections, e.g. the choice of k nearest neighbor connectivity of the graph.
Denoising based on wavelets and deblurring via self organizing map for Synthetic Aperture Radar images
Venue: ArXiv

This work deals with unsupervised image deblurring. We present a new deblurring procedure on images provided by low resolution synthetic aperture radar (SAR) or simply by multimedia in presence of multiplicative (speckle) or additive noise, respectively. The method we propose is defined as a two step process. First, we use an original technique for noise reduction in wavelet domain. Then, the learning of a Kohonen self organizing map (SOM) is performed directly on the denoised image to take out it the blur. This technique has been successfully applied to real SAR images, and the simulation results are presented to demonstrate the effectiveness of the proposed algorithms.
Analysis of Wavelets Based Compression in 1D Signals, 2D and 3D Images

This paper deals with a analysis and study of denoising of one dimensional voice signal and also compression and decomposition of two dimensional and three dimensional images using wavelets. Analysis of image and data compression are rapidly used in field of Medical data analysis, Telemedicine, ECG analysis, Texture analysis etc. The analysis is based on transform of signal and image using discrete wavelet transform (DWT) algorithm. The performance of this algorithm is better achieved for wavelet based compression of signals and images. A simulation result shows the technique of DWT which provides sufficient high compression ratio. Keywords compression, DWT, decomposition, de noising, wavelets.
A Comparative Study of Mechanical Properties and Microstructures of FSW joints with Conventional welded joints

Energy savings and environmental preservation are important issues for us to resolve. Since reducing the weight of vehicles is one of the efficient measures, the use of the combination of steel and aluminium alloy has been increasing in fabricating vehicles. Under this situation, many trials to weld steel to aluminium alloy have been conducted. However, sound joints have not been produced so far, because hard and brittle intermetallic compounds were formed at the weld whenever steel was welded to aluminium by fusion welding. In gas welding process the metals get heated and due to the heating of the metal the strength of the metal decreases and also the microstructure of the metal changes. But in friction stir welding the metal gets less heated compared to the gas welding process. Due to the less heat absorbed by the metal the strength and microstructure changes comparatively less than the gas welding process. So comparatively friction stir welding gives accurate microstructure and tensile strength than the gas welding process. In the present work we tried to compare the micro structure, micro hardness and tensile strength of two Friction Stir welded joint with the gas welded joint.  Stir Welding (FSW) Al alloys, mechanical properties, microstructure.
Service composition based on discrete particle swarm optimization in military organization cloud cooperation

This paper addresses the problem of service composition in military organization cloud cooperation(MOCC) Military service providers(MSP) cooperate together to provide military resources for military service users(MSU) A group of atom services, each of which has its level of quality of service(QoS) can be combined together into a certain structure to form a composite service. Since there are a large number of atom services having the same function, the atom service is selected to participate in the composite service so as to fulfill users' will. In this paper a method based on discrete particle swarm optimization(DPSO) is proposed to tackle this problem. The method aims at selecting atom services from service repositories to constitute the composite service, satisfying the MSU's requirement on QoS. Since the QoS criteria include location aware criteria and location independent criteria, this method aims to get the composite service with the highest location aware criteria and the best match location independent criteria. Simulations show that the DPSO has a better performance compared with the standard particle swarm optimization(PSO) and genetic algorithm(GA)
All you need is a good init
Venue: ICLR

Layer sequential unit variance (LSUV) initialization a simple method for weight initialization for deep net learning is proposed. The method consists of the two steps. First, pre initialize weights of each convolution or inner product layer with orthonormal matrices. Second, proceed from the first to the final layer, normalizing the variance of the output of each layer to be equal to one. Experiment with different activation functions (maxout, ReLU family, tanh) show that the proposed initialization leads to learning of very deep nets that (i) produces networks with test accuracy better or equal to standard methods and (ii) is at least as fast as the complex schemes proposed specifically for very deep nets such as FitNets (Romero et al. (2015) and Highway (Srivastava et al. (2015) Performance is evaluated on GoogLeNet, CaffeNet, FitNets and Residual nets and the state of the art, or very close to it, is achieved on the MNIST, CIFAR 10/100 and ImageNet datasets.
Sentiment Analysis in Social Networks

The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large scale, short, noisy, context dependent and dynamic nature. Further, this volume: Takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologiesProvides insights into opinion spamming, reasoning, and social network analysisShows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequencesServes as a one stop reference for the state of the art in social media analyticsTakes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologiesProvides insights into opinion spamming, reasoning, and social network miningShows how to apply opinion mining tools for a particular application and domain, and how to get the best results for understanding the consequencesServes as a one stop reference for the state of the art in social media analytics