nils-herrmann/clean_scirepeval huggingface dataset:
Implementation of the Power to Methanol concept by using CO2 from lignite power plants: Techno economic investigation

The scope of this study is the techno economic evaluation of the Power to Methanol (PtM) concept, valorising CO2 that comes from high CO2 emitting lignite fired power plants, in order to investigate the prospect of PtM in a future energy market of high RES penetration and advanced lignite fired power plants. The concept was examined for the case of Greece. Two distinct case studies were selected, regarding the entity that makes the investment in PtM: a) the power plant owner case and b) the private investor case. The study focused on the estimation of the MeOH cost for a marginally feasible investment by fulfilling techno economic criteria. In the case of the power plant owner the MeOH cost was estimated at 421EUR/tn MeOH and was about 40% lower than the cost calculated for the private investor case (580EUR/tn MeOH) due to the higher operating capacity of the H2 and MeOH plants and the lower electricity cost in the first case. The MeOH cost was further decreased in both cases by considering: a) large plant scales, b) low electricity cost, c) high operating time for the H2 and MeOH plants, d) low CO2 costs and/or e) low discount rates. Following the results for Greece, the power plant owner case was also examined for Germany and proved to be even more sustainable, due to the lower electricity cost in Germany.
Computational Health Informatics in the Big Data Age
Venue: ACM Comput. Surv.

The explosive growth and widespread accessibility of digital health data have led to a surge of research activity in the healthcare and data sciences fields. The conventional approaches for health data management have achieved limited success as they are incapable of handling the huge amount of complex data with high volume, high velocity, and high variety. This article presents a comprehensive overview of the existing challenges, techniques, and future directions for computational health informatics in the big data age, with a structured analysis of the historical and state of the art methods. We have summarized the challenges into four Vs (i.e. volume, velocity, variety, and veracity) and proposed a systematic data processing pipeline for generic big data in health informatics, covering data capturing, storing, sharing, analyzing, searching, and decision support. Specifically, numerous techniques and algorithms in machine learning are categorized and compared. On the basis of this material, we identify and discuss the essential prospects lying ahead for computational health informatics in this big data age.
Machine Learning for Health Informatics
Venue: Lecture Notes in Computer Science

Machine Learning (ML) studies algorithms which can learn from data to gain knowledge from experience and to make decisions and predictions. Health Informatics (HI) studies the effective use of probabilistic information for decision making. The combination of both has greatest potential to rise quality, efficacy and efficiency of treatment and care. Health systems worldwide are confronted with "big data" in high dimensions, where the inclusion of a human is impossible and automatic ML (aML) show impressive results. However, sometimes we are confronted with complex data, "little data" or rare events, where aMLapproaches suffer of insufficient training samples. Here interactive ML (iML) may be of help, particularly with a doctor in the loop, e.g. in subspace clustering, k Anonymization, protein folding and protein design. However, successful application of ML for HI needs an integrated approach, fostering a concerted effort of four areas: (1) data science, (2) algorithms (with focus on networks and topology (structure) and entropy (time) (3) data visualization, and last but not least (4) privacy, data protection, safety security.
The Science of Female Orgasms: Pleasing Female Partners in Casual and Long Term Relationships

The occurrence of the female orgasm, or lack thereof, has fueled productive research regarding gender differences in sexuality. Both scientific research and popular beliefs support the idea that men orgasm more frequently than women do and, given the biological nature of the orgasm phase of sexual responses, it is easy to assume that the gender difference in orgasm frequency is "natural" and unresponsive to contextual factors (e.g. relationship styles) While it may be true that men orgasm more frequently than women in partnered sex, using sociocultural approaches to understanding orgasm, we argue that it is inaccurate to suggest that men's orgasms are easier and women's are more difficult to invoke. In other words, yes men orgasm more frequently than women but this is not because women are biologically hardwired not to orgasm or receive sexual pleasure. Yet remains the question: Why do women orgasm less frequently than men do? What factors might narrow this gap in orgasm frequency between men and women? In this chapter, we critically examine gender differences in orgasm and, in doing so, provide science based suggestions for individuals and relationship partners to close the orgasm gap between heterosexual men and women. We will begin our examination of women's orgasm with providing an overview anatomy and orgasm. For the remainder of the chapter, we consider orgasm frequency in the context of both short term (casual sex and hookup scenarios) and long term relationships. Throughout, we offer suggestions for women and their sexual partners to increase women's orgasm frequency across both short term and long term relationships.
Long tail Vocabulary Dictionary Extraction from the Web
Venue: WSDM

A dictionary a set of instances belonging to the same conceptual class is central to information extraction and is a useful primitive for many applications, including query log analysis and document categorization. Considerable work has focused on generating accurate dictionaries given a few example seeds, but methods to date cannot obtain long tail (rare) items with high accuracy and recall. In this paper, we develop a novel method to construct high quality dictionaries, especially for long tail vocabularies, using just a few user provided seeds for each topic. Our algorithm obtains long tail (i.e. rare) items by building and executing high quality webpage specific extractors. We use webpage specific structural and textual information to build more accurate per page extractors in order to detect the long tail items from a single webpage. These webpage specific extractors are obtained via a co training procedure using distantly supervised training data. By aggregating the page specific dictionaries of many webpages, Lyretail is able to output a high quality comprehensive dictionary. Our experiments demonstrate that in long tail vocabulary settings, we obtained a 17.3% improvement on mean average precision for the dictionary generation process, and a 30.7% improvement on F1 for the page specific extraction, when compared to previous state of the art methods.
Large Scale Distributed Semi Supervised Learning Using Streaming Approximation
Venue: AISTATS

Traditional graph based semi supervised learning (SSL) approaches, even though widely applied, are not suited for massive data and large label scenarios since they scale linearly with the number of edges |E| and distinct labels $m$ To deal with the large label size problem, recent works propose sketch based methods to approximate the distribution on labels per node thereby achieving a space reduction from $O(m) to $O(\log m) under certain conditions. In this paper, we present a novel streaming graph based SSL approximation that captures the sparsity of the label distribution and ensures the algorithm propagates labels accurately, and further reduces the space complexity per node to $O(1) We also provide a distributed version of the algorithm that scales well to large data sizes. Experiments on real world datasets demonstrate that the new method achieves better performance than existing state of the art algorithms with significant reduction in memory footprint. We also study different graph construction mechanisms for natural language applications and propose a robust graph augmentation strategy trained using state of the art unsupervised deep learning architectures that yields further significant quality gains.
Twin support vector machines based on fruit fly optimization algorithm
Venue: Int. J. Mach. Learn. Cybern.

Twin support vector machine (TWSVM) which solves classification problems through constructing two nonparallel planes by solving two related SVM type problems, has become a hot spot in the field of machine learning. In addition to keeping the advantages of SVM, the classification performance of TWSVM is also significantly better than that of SVM. However, there are at least two parameters in TWSVM which need to specify. These parameters make great difference to the classification result of TWSVM and to find the optimal parameters is an important but difficult work. In order to overcome this deficiency, in this paper, we propose the twin support vector machines based on Fruit Fly Optimization Algorithm (FOA TWSVM) This algorithm uses FOA TWSVM, which has high optimization ability and small computation complexity, to select the parameters for TWSVM. The experimental results show that this algorithm is able to find the suitable parameters, and has higher classification accuracy compared with some other algorithms.
Encyclopedia of Parasitology
Venue: Springer Berlin Heidelberg

Knowledge in the field of parasitology must be kept at a high level and up to date in order to fight a parasitosis as quickly and effectively as possible. The third edition of this, one of Springer's renowned and authoritative Major Reference Works, contributes to these goals in several ways. First, the number of entries has been increased by about 30% Secondly the content has been improved even more by adding additional tables and figures. Thirdly, the extensive linking between definitions and essays facilitates information within a minimum of time. More than 40 international contributors, who are well known specialists in their fields, give a comprehensive review of all parasites and therapeutic strategies in veterinarian and human parasitology.
Variability Analysis of Therapeutic Movements using Wearable Inertial Sensors
Venue: Journal of Medical Systems

A variability analysis of upper limb therapeutic movements using wearable inertial sensors is presented. Five healthy young adults were asked to perform a set of movements using two sensors placed on the upper arm and forearm. Reference data were obtained from three therapists. The goal of the study is to determine an intra and inter group difference between a number of given movements performed by young people with respect to the movements of therapists. This effort is directed toward studying other groups characterized by motion impairments, and it is relevant to obtain a quantified measure of the quality of movement of a patient to follow his/her recovery. The sensor signals were processed by applying two approaches, time domain features and similarity distance between each pair of signals. The data analysis was divided into classification and variability using features and distances calculated previously. The classification analysis was made to determine if the movements performed by the test subjects of both groups are distinguishable among them. The variability analysis was conducted to measure the similarity of the movements. According to the results, the flexion/extension movement had a high intra group variability. In addition, meaningful information were provided in terms of change of velocity and rotational motions for each individual.
A Frequency and Pattern Reconfigurable Center Shorted Microstrip Antenna
Venue: IEEE Antennas and Wireless Propagation Letters

A frequency and pattern reconfigurable antenna based on a center shorted microstrip patch is presented. The novel design utilizes two resonance modes of a microstrip antenna with shorting vias at the patch center. To set up the antenna tuning mechanism, two groups of varactors with a measured tuning range of [0.149, 1.304] pF are placed at two opposite sides of the antenna, followed by open circuited loading stubs. For a particular bias voltage configuration, the structure operates as a dual band antenna with broadside radiation at the upper resonance frequency and monopole like radiation at the lower band. By varying the dc bias voltage, both resonance frequencies can be changed simultaneously. Based on the proposed concept, a demonstration antenna has been designed so that the two types of aforementioned patterns can be reconfigured across a continuous fractional frequency range of more than 20% Experimental results are provided, which validate the proposed concept and design procedure.
Novel Design and Performance Analysis of Rectangular Frequency Reconfigurable Micro Strip Patch Antenna using Line Feed technique for Wireless Applications

This paper deals with a new design approach for achieving reconfigurable function in Microstrip Patch Antennas.  Initially, the antennas are designed on three independent substrates for three individual resonant frequencies such as 2.45GHz (Wi Fi, Bluetooth, and RF ID) 5.5GHz (Wi Fi, WLAN, and Wi Max) and 7.5GHz (Indoor UWB wireless applications, Satellite and Marine Radar) using line feed technique for various wireless communications. And then, a new frequency reconfigurable rectangular Microstrip Patch Antenna is designed with three patches which are installed on a single substrate using the same feeding technique, where RF power is fed directly to the center patch. The integration of patches on the substrate is made possible with RF switches.  The performance parameters such as return loss, bandwidth and radiation pattern of the reconfigured antenna were measured based on the RF switch on/off conditions.  Based on the return loss and bandwidth, the performances of both the reconfigurable and non reconfigurable antenna designs are compared, tabulated and presented using ANSOFT HFSS 13.0.
A similarity measure of intuitionistic fuzzy soft sets and its application in medical diagnosis
Venue: Appl. Soft Comput.

Graphical abstractDisplay Omitted HighlightsThe proposed method is applied on UCI Machine Learning Repository datasets and its corresponding performance measures are obtained.The comparison of the proposed similarity measures with the existing results reveal that the accuracy obtained by the present technique provides better similarity rating.It is also observed that fixing the threshold values for the similarity measures is difficult due to the negative rating in some existing methodology.The above difficulty can be avoided by using this newly proposed technique, in which the similarity measures lies between 0 and 1.The similarity rating obtained by the suggested technique is compared with the class distribution data set and are analyzed.The proposed method exhibits more accuracy, sensitivity, ROC curves, AUC values and enhanced F measure than the existing methods. In this paper, a new similarity measure and a weighted similarity measure on intuitionistic fuzzy soft sets (IFSSs) are proposed and some of their basic properties are discussed. Using the proposed similarity measure, a relation ?a) between two IFSSs are defined and it is found that the defined relation is not an equivalence relation. Further, the effectiveness of the proposed similarity measure is demonstrated in a numerical example with the help of measure of performance and measure of error. Moreover, medical diagnosis problems have been exhibited through a hypothetical case study by using this proposed similarity measure. Finally, the proposed method is applied to 10 different medical data sets from UCI Machine Learning Repository datasets and its similarity measures are calculated. The corresponding performance measures, like, accuracy, sensitivity, specificity, ROC curves, AUC values, and F measures are obtained and it is compared with the existing methods. This shows that the proposed method exhibits more accuracy, sensitivity and enhanced F measures than the existing methods.
Hesitant fuzzy information measures and their applications in multi criteria decision making
Venue: Int. J. Syst. Sci.

Hesitant fuzzy set (HFS) is a powerful decision tool to express uncertain information more flexibly and comprehensively. The aim of this paper is to propose more reasonable information measures for HFSs in comparison with the existing ones. First, a series of distance measures is suggested for hesitant fuzzy element and hesitant fuzzy sets. These measures are directly calculated from hesitant fuzzy elements without judging the decision makers' risk preference and adding any values into the hesitant fuzzy element with the smaller number of elements. Then, some similarity and entropy measures are proposed based on the transforming relationship among the information measures. Additionally, based on the proposed information measures, a TOPSIS method for hesitant fuzzy information is provided. Finally, some numerical examples are used in order to illustrate the proposed decision method and a comparative analysis is made to demonstrate that the suggested measures are more objective and feasible in certain cases.
Discovery of a Partner Affair and Major Depressive Episode in a Probability Sample of Married or Cohabiting Adults.
Venue: Family process

Prior research has found that humiliating marital events are associated with depression. Building on this research, the current study investigated the association between one specific humiliating marital event discovering that one's partner had an affair and past year major depressive episode (MDE) in a probability sample of married or cohabiting men and women who were at high risk for depression based on the criterion that they scored below the midpoint on a measure of marital satisfaction (N 227) Results indicate that (i) women were more likely than men to report discovering their partner had an affair in the prior 12 months; (ii) discovering a partner affair was associated with a higher prevalence of past year MDE and a lower level of marital adjustment; and (iii) the association between discovering a partner affair and MDE remained statistically significant when holding constant demographic variables and marital adjustment. These results support continued investigation into the impact that finding out about an affair has on the mental health of the person discovering a partner affair.
Mining Performance Regression Inducing Code Changes in Evolving Software
Venue: 2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)

During software evolution, the source code of a system frequently changes due to bug fixes or new feature requests. Some of these changes may accidentally degrade performance of a newly released software version. A notable problem of regression testing is how to find problematic changes (out of a large number of committed changes) that may be responsible for performance regressions under certain test inputs.We propose a novel recommendation system, coined as PefImpact, for automatically identifying code changes that may potentially be responsible for performance regressions using a combination of search based input profiling and change impact analysis techniques. PefImpact independently sends the same input values to two releases of the application under test, and uses a genetic algorithm to mine execution traces and explore a large space of input value combinations to find specific inputs that take longer time to execute in a new release. Since these input values are likely to expose performance regressions, PefImpact automatically mines the corresponding execution traces to evaluate the impact of each code change on the performance and ranks the changes based on their estimated contribution to performance regressions. We implemented PefImpact and evaluated it on different releases of two open source web applications. The results demonstrate that PefImpact effectively detects input value combinations to expose performance regressions and mines the code changes are likely to be responsible for these performance regressions.
Dynamic Relationship Marketing

Firms routinely engage in relationship marketing (RM) efforts to improve their relationships with business partners, and extant research has documented the effectiveness of various RM strategies. According to the perspective proposed in this article, as customers migrate through different relationship states over time, not all RM strategies are equally effective, so it is possible to identify the most effective RM strategies given customers' states. The authors apply a multivariate hidden Markov model to a six year longitudinal data set of 552 business to business relationships maintained by a Fortune 500 firm. The analysis identifies four latent buyer seller relationship states, according to each customer's level of commitment, trust, dependence, and relational norms, and it parsimoniously captures customers' migration across relationship states through three positive (exploration, endowment, recovery) and two negative (neglect, betrayal) migration mechanisms. The most effective RM strategies across migration paths can help firms promote customer migration to higher performance states and prevent deterioration to poorer ones. A counterfactual elasticity analysis compares the relative importance of different migration strategies at various relationship stages. This research thus moves beyond extant RM literature by focusing on the differential effectiveness of RM strategies across relationship states, and it provides managerial guidance regarding efficient, dynamic resource allocations.
Does relationship marketing matter in online retailing? A meta analytic approach

Building on the meta analytic model suggested by Palmatier et al. Journal of Marketing, 70, 136 153, (2006) this study extends the relationship marketing framework to the domain of online retailing to identify what strategies help build relationships with online customers. Specifically, this meta analytic study identifies key antecedents and consequences of relationship marketing in online retailing. The study also examines the relationship between the four mediators trust, commitment, relationship quality, and relationship satisfaction and the antecedents and consequences of relationship marketing. Similarity and seller expertise were found to have the strongest impact on relational mediators, and word of mouth was the most critical outcome of relationship marketing efforts. The model proffered in this study will motivate hypotheses to be examined by future researchers. The model also helps managers to identify the key drivers of relationship marketing in online retailing.