nils-herrmann/clean_scirepeval huggingface dataset:
Effect of Consumption Emotion on Hotel and Resort Spa Experience
Year: 2014

Very few studies focus on the behavior of hotel and resort spa customers in China. Hence, this research proposes a model for testing the impact of spa service quality, positive and negative consumption emotions, and hedonic and utilitarian perceived value on the behavioral intentions to visit a spa. The valid sample comprised 487 customers of five star hotel/resort spas in China. Data were obtained from the self administered survey answered by respondents constituting the sample. This study identified the vital function of consumption emotion, especially positive emotion dimension. The results indicated the mediating influence of positive emotion on connecting service quality and creating the perceived value of consumers as well as behavioral intention in the hotel and resort spa context.
Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations
Venue: Front. Neurosci.
Year: 2019

Gliomas are the most common primary malignant brain tumors in adults. Accurate grading is crucial as therapeutic strategies are often disparate for different grades and may influence patient prognosis. This study aims to provide an automated glioma grading platform on the basis of machine learning models. In this paper, we investigate contributions of multi parameters from multimodal data including imaging parameters or features from the Whole Slide images (WSI) and the proliferation marker Ki 67 for automated brain tumor grading. For each WSI, we extract both visual parameters such as morphology parameters and sub visual parameters including first order and second order features. On the basis of machine learning models, our platform classifies gliomas into grades II, III, and IV. Furthermore, we quantitatively interpret and reveal the important parameters contributing to grading with the Local Interpretable Model Agnostic Explanations (LIME) algorithm. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. The performance of our grading model was evaluated with cross validation, which randomly divided the patients into non overlapping training and testing sets and repeatedly validated the model on the different testing sets. The primary results indicated that this modular platform approach achieved the highest grading accuracy of 0.90 0.04 with support vector machine (SVM) algorithm, with grading accuracies of 0.91 0.08, 0.90 0.08, and 0.90 0.07 for grade II, III, and IV gliomas, respectively.
STOCK MARKET PRICE PREDICTION SYSTEM USING NEURAL NETWORKS AND GENETIC ALGORITHM
Year: 2019

Stock market forecasting of price/index has always been an important financial subject. Knowing the close price/index based on previous information is useful for investors who need to buy or sell the stock. Most of the applications are focused on building systems with less error and more accuracy. Most traders have used technical analysis tools to predict future stock market movements. Popular methods to find dynamic relationship between input and target output were artificial neural networks that proved to be effective recently. The evolutionary algorithms improve performance in predicting financial market results. This study uses the following: ten technical indicators as inputs, genetic algorithm (GA) to select significant features, backpropagation neural (BPN) to predict future stock price based on features of the previous day and self organizing map (SOM) to reduce data size. Also, this paper compares three fusion hybrid prediction models, SOM GA BPN, SOM BPN and GA BPN, with a single model, which is the BPN. Three indices (S&P 500, IBM and NASDAQ) are used in order to evaluate the performance of the proposed hybrid methods. We compare these models with other models such as Support Vector Regression (SVR) Artificial Neural Network (ANN) Random Forest (RF) SVR ANN, SVR RF and SVR SVR fusion prediction models using evaluation measures. The comparison proves the effectiveness and accuracy of the proposed technical indicators and methods.
Disintegrated Rural Development? Neo endogenous Rural Development, Planning and Place Shaping in Diffused Power Contexts
Year: 2010

What is the role of the state in promoting sustainable rural communities? Only a few years ago any discussion of this question would have alluded to the concept of integrated rural development. Today the concept of governance is internationally used to address such questions, reflecting a recognition of the changing role of the state (at all levels) and the greater propensity for public, private and voluntary sectors to interact at multiple scales in diffused power contexts together with attempts to mobilise local actors. This article asks whether the concept, integrated rural development, still has any meaning in the context of the new rural governance and begins to link this to re theorisations of concepts of spatial planning, place shaping, capacity building and neo endogenous development, and offers illustrations from the north of Scotland. The article concludes by suggesting that initiatives such as the EU's LEADER programme might be recast explicitly as a transnational experiment in doing 'disintegrated rural development' addressing the challenges of neo endogenous rural development, multi scalar governance, an enabling, generative state and the transformation of mainstream policies.
Simultaneous determination of 117 pesticides and 30 mycotoxins in raw coffee, without clean up, by LC ESI MS/MS analysis.
Venue: Analytica chimica acta
Year: 2018

This paper describes the optimization and validation of an acetonitrile based method for simultaneous extraction of multiple pesticides and mycotoxins from raw coffee beans followed by LC ESI MS/MS determination. Before extraction, the raw coffee samples were milled and then slurried with water. The slurried samples were spiked with two separate standard solutions, one containing 131 pesticides and a second with 35 mycotoxins, which were divided into 3 groups of different relative concentration levels. Optimization of the QuEChERS approach included performance tests with acetonitrile acidified with acetic acid or formic acid, with or without buffer and with or without clean up of the extracts before LC ESI MS/MS analysis. For the clean up step, seven d SPE sorbents and their various mixtures were evaluated. After method optimization a complete validation study was carried out to ensure adequate performance of the extraction and chromatographic methods. The samples were spiked at 3 concentrations levels with both mycotoxins and pesticides (with 6 replicates at each level, n 6) and then submitted to the extraction procedure. Before LC ESI MS/MS analysis, the acetonitrile extracts were diluted 2 fold with methanol, in order to improve the chromatographic performance of the early eluting polar analytes. Calibration standard solutions were prepared in organic solvent and in blank coffee extract at 7 concentration levels and analyzed 6 times each. The method was assessed for accuracy (recovery precision (RSD% selectivity, linearity (r2) limit of quantification (LOQ) and matrix effects
The eu Seasonal Workers Directive: When Immigration Controls Meet Labour Rights
Year: 2014

The Seasonal Workers Directive combines immigration law, which regulates entry and stay in a territory, with labour law, which governs the rights of workers. The different interests and expertise of the various eu institutions involved in the Directive's drafting and adoption exacerbated the tension between these two legal fields. In turn, this tension compromised the achievement of several of the eu's explicit objectives, namely, creating a level playing field for the recruitment of seasonal migrant workers across the Member States, instituting a circular migration program, and protecting migrant workers from economic and social exploitation. This article focuses on the extent to which the Directive has the capacity to protect seasonal migrant workers. To do so, it sketches the history of the Directive and discusses some consequences of its treaty basis, which provides the context for our analysis and evaluation of the substantive provisions of the Directive.
Integrating nurses' experiences with supporting behaviour change for cardiovascular prevention into a self management internet platform in Finland and the Netherlands: a qualitative study
Venue: BMJ Open
Year: 2019

Global ageing is linked to an increased burden of cardiovascular disease and dementia, which calls for better prevention strategies. Self management and eHealth applications are regarded as promising strategies to support prevention. The aim of this study was to explore nurses' best practices concerning behaviour change guidance for cardiovascular (CV) prevention in order to learn how to optimally integrate them into a coach supported internet platform for CV self management.  Qualitative focus group study in Finland and the Netherlands. Discussions were audiotaped and transcribed. Data were thematically analysed following principles of grounded theory.  Dutch and Finnish primary care settings.  Six Finnish and seven Dutch primary care nurses with experience in CV prevention.  Similar best practices were found in both countries and comprised of (1) establishing a relationship of trust, (2) managing awareness and expectations and (3) appropriate timing and monitoring of the process of behaviour change. However, the Finnish and Dutch nurses used different approaches for accomplishment of these practices, which was reflected in their recommendations for online support. Both groups emphasised that online support should be combined with human support and integrated into regular care. Finnish nurses had more confidence in patient self management and remote communication than Dutch nurses, who emphasised the importance of face to face contact and preferred to keep control of medical aspects of prevention.  Differences in Dutch and Finnish's nurses' practices for supporting CV prevention appear to reflect their local healthcare practices, which should be taken into account when designing internet platforms for health self management. Including cognitive health as a goal of CV prevention might stimulate motivation for health behaviour change.
Sentiment Classification for Hindi Tweets in a Constrained Environment Augmented Using Tweet Specific Features
Venue: MIKE
Year: 2015

India being a diverse country rich in spoken languages with around 23 official languages has always left open a wide arena for NLP researchers. The increase in the availability of voluminous data in Indian languages in the recent years has prompted researchers to explore the challenges in the Indian language domain. The proposed work explores Sentiment Analysis on Hindi tweets in a constrained environment and hence proposes a model for dealing with the challenges in extracting sentiment from Hindi tweets. The model has exhibited an average performance with cross validation accuracy for training data around 56i 3/4 and a test accuracy of 43i 3/4
Dynamic Dispatching for Large Scale Heterogeneous Fleet via Multi agent Deep Reinforcement Learning
Venue: 2020 IEEE International Conference on Big Data (Big Data)
Year: 2020

Dynamic dispatching is one of the core problems for operation optimization in traditional industries such as mining, as it is about how to smartly allocate the right resources to the right place at the right time. Conventionally, the industry relies on heuristics or even human intuitions which are often short sighted and sub optimal solutions. Leveraging the power of AI and Internet of Things (IoT) data driven automation is reshaping this area. However, facing its own challenges such as large scale and heterogenous trucks running in a highly dynamic environment, it can barely adopt methods developed in other domains (e.g. ride sharing) In this paper, we propose a novel Deep Reinforcement Learning approach to solve the dynamic dispatching problem in mining. We first develop an event based mining simulator with parameters calibrated in real mines. Then we propose an experience sharing Deep Q Network with a novel abstract state/action representation to learn memories from heterogeneous agents altogether and realizes learning in a centralized way. We demonstrate that the proposed methods significantly outperform the most widely adopted approaches in the industry by 5.56% in terms of productivity. The proposed approach has great potential in a broader range of industries (e.g. manufacturing, logistics) which have a large scale of heterogenous equipment working in a highly dynamic environment, as a general framework for dynamic resource allocation.
Cosine Distance Metric Learning for Speaker Verification Using Large Margin Nearest Neighbor Method
Venue: PCM
Year: 2014

In this paper, a novel cosine similarity metric learning based on large margin nearest neighborhood LMNN is proposed for an i vector based speaker verification system. Generally, in an i vector based speaker verification system, the decision is based on the cosine distance between the test i vector and target i vector. Metric learning methods are employed to reduce the within class variation and maximize the between class variation. In this proposed method, cosine similarity large margin nearest neighborhood CSLMNN metric is learned from the development data. The test and target i vectors are linearly transformed using the learned metric. The objective of learning the metric is to ensure that the k nearest neighbors that belong to the same speaker are clustered together, while impostors are moved away by a large margin. Experiments conducted on the NIST 2008 and YOHO databases show improved performance compared to speaker verification system, where no learned metric is used.
Of Mice, Birds, and Men: The Mouse Ultrasonic Song System Has Some Features Similar to Humans and Song Learning Birds
Venue: PloS one
Year: 2012

Humans and song learning birds communicate acoustically using learned vocalizations. The characteristic features of this social communication behavior include vocal control by forebrain motor areas, a direct cortical projection to brainstem vocal motor neurons, and dependence on auditory feedback to develop and maintain learned vocalizations. These features have so far not been found in closely related primate and avian species that do not learn vocalizations. Male mice produce courtship ultrasonic vocalizations with acoustic features similar to songs of song learning birds. However, it is assumed that mice lack a forebrain system for vocal modification and that their ultrasonic vocalizations are innate. Here we investigated the mouse song system and discovered that it includes a motor cortex region active during singing, that projects directly to brainstem vocal motor neurons and is necessary for keeping song more stereotyped and on pitch. We also discovered that male mice depend on auditory feedback to maintain some ultrasonic song features, and that sub strains with differences in their songs can match each other's pitch when cross housed under competitive social conditions. We conclude that male mice have some limited vocal modification abilities with at least some neuroanatomical features thought to be unique to humans and song learning birds. To explain our findings, we propose a continuum hypothesis of vocal learning.
Hollow Au/Polypyrrole Capsules to Form Porous and Neural Network Like Nanofibrous Film for Wearable, Super Rapid, and Ultrasensitive NH3 Sensor at Room Temperature.
Venue: ACS applied materials interfaces
Year: 2020

Wearable conducting polymer based NH3 sensors are highly desirable in real time environmental monitoring and human health protection but still a challenge for their relatively long response/recovery time and moderate sensitivity at room temperature. Herein, we present an effective route to fulfill this challenge by constructing porous and neural network like Au/polypyrrole (Au/PPy) electrospun nanofibrous film with hollow capsular units for NH3 sensor. Taking the unique architecture and synergistic effect between Au and PPy, our sensor exhibits not only super rapid response/recovery time (both ~7 s) faster than all reported sensors, but also stable and ultrahigh sensitivity (response reaches ~2.3 for 1 ppm NH3) at room temperature even during repeated deformation. Furthermore, good selectivity has been also achieved. These outstanding properties make our sensor hold great potential in real time NH3 related disease diagnosis and environmental monitoring at room temperature.
Length Weight relationship and relative condition factor of Anabas testudineus (Bloch) of Deepar Beel (wetland) of Assam, India
Year: 2015

The present study reports the length weight relationship of male and female Anabas testudineus (Bloch) of Deepar Beel (wetland) of Assam. The fishes are separated into both male and female for calculation of length weight relationship and relative condition factor. The growth performance of length weight relationship are found high since the correlation coefficient 'r' exhibits high degree of relationship with positive allometric correlation in both male and female Anabas testudineus. The Kn value of Male Anabas testudineus ranges between 0.79 and 1.42 with an average of 0.98+ 0.13 while in female it ranges between 0.71 and 1.29 with an average of 1.02+ 0.11. The relative condition factor in male is observed to increase from lighter to heavier fish. However, in female the relative condition factor initially declines from lighter to heavier fish and exhibits the lowest value at medium sized fish and thereafter steadily inclines to get the highest value in bigger fish having size range of 13 cm TL.
Experimental investigation on the start up characteristics of single loop thermosyphon for motorized spindle bearing shaft system cooling
Year: 2020

is a promising method for handling the thermal dissipation problem of motorized spindle bearing shaft system. The SLTP used for motorized spindle shaft cooling is with different structure from that of the existing SLTP. Therefore, it is necessary to conduct comprehensive study for its working mechanism. A visualization and thermal characterization experiment was carried out to research the effect of different heat load (Q) and liquid filling ratio (FR) on the start up characteristics of the SLTP in this paper. The results show that the heat transfer in the SLTP with high FR at low heat load is intermittent boiling. The working fluid oscillates at the boiling incipience due to the U shape structure of evaporator section, and temperature of the evaporator wall has an overshoot because of subcooled fluid and "interfacial vaporization heat sink effect" At high heat load, the SLTP with high FR will translate from natural convection to nucleate boiling when starting, while liquid in the storage pool will be pushed into condenser section by vapor because of the excessive liquid level. However, the SLTP with low FR will not boil at low heat load, it forms a circulation mode of natural convection and condensation. With the increase of heat load, the heat transfer mechanism of the SLTP with low FR develops into nucleate boiling, and the liquid film returning from condenser section will be boiling when flows through evaporator section. In present experiment, the SLTP with 30% FR has the best start up and flow performance.
Efficient and Privacy Preserving Contact Tracing System for Covid 19 using Blockchain
Venue: 2021 IEEE International Conference on Communications Workshops (ICC Workshops)
Year: 2021

COVID 19 has been endangering people's lives and causing a huge burden on the healthcare sector. To limit the fast spread of the virus, contact tracing systems are currently adopted worldwide to identify the close contacts of positive cases during the incubation period of the virus and notify them to quarantine. However, the existing systems are vulnerable to security and privacy attacks and suffer from large communication and computation overheads. To address these limitations, in this paper, we propose an efficient and privacy preserving contact tracing system based on a consortium Blockchain. Instead of depending on one entity to run the system, our system is run in a decentralized fashion by multiple health authorities that construct the Blockchain network. The utilization of Blockchain secures our system against the problems of centralized architecture. Besides, our system is designed to thwart the false reporting (panic) attack, while preserving the privacy of the users against identification and social graph disclosure attacks. The performance evaluation of our system demonstrates its efficiency in terms of communication and computation.
FOCUS: A Recommender System for Mining API Function Calls and Usage Patterns
Venue: 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)
Year: 2019

Software developers interact with APIs on a daily basis and, therefore, often face the need to learn how to use new APIs suitable for their purposes. Previous work has shown that recommending usage patterns to developers facilitates the learning process. Current approaches to usage pattern recommendation, however, still suffer from high redundancy and poor run time performance. In this paper, we reformulate the problem of usage pattern recommendation in terms of a collaborative filtering recommender system. We present a new tool, FOCUS, which mines open source project repositories to recommend API method invocations and usage patterns by analyzing how APIs are used in projects similar to the current project. We evaluate FOCUS on a large number of Java projects extracted from GitHub and Maven Central and find that it outperforms the state of the art approach PAM with regards to success rate, accuracy, and execution time. Results indicate the suitability of context aware collaborative filtering recommender systems to provide API usage patterns.
The use of virtual reality (VR) exposure for reducing contamination fear and disgust: Can VR be an effective alternative exposure technique to in vivo?
Year: 2020

Virtual reality (VR) has become a new tool in psychological research and application. Although several studies have investigated its triggering and reducing role in anxiety via VR based exposure and response prevention (VR ERP) across different psychopathologies, its efficiency in contamination fear and reduction of disgust are yet to be examined. The study contributes to the field by investigating the effectiveness of the VR ERP on the reduction of anxiety, disgust and urge to wash levels using an experimental design.  A total of 21 non clinical participants with high contamination fear were randomly assigned to experimental (n 9) and control (n 12) groups. The experimental group was exposed to repeated VR ERP sessions.  After a minimum of three VR ERP sessions, the experimental group scored significantly lower than the control group on the anxiety, disgust, and urge to wash hands scores.  A small sample size, an analogue sample, and a generic virtual scenario content were potential limitations of the study.  The results indicated that the VR ERP can be an efficient and alternative exposure tool in the reduction of anxiety, disgust, and urge to wash hands. The theoretical and clinical applications of the VR ERP in the treatment of contamination based OCD symptoms were discussed in light of our findings.