nils-herrmann/clean_scirepeval huggingface dataset:
Gaps Analysis of Integrating Product Design, Manufacturing, and Quality Data in The Supply Chain Using Model Based Definition.
Venue: Proceedings of the ASME International Conference on Manufacturing Science and Engineering. ASME International Conference on Manufacturing Science and Engineering

Advances in information technology triggered a digital revolution that holds promise of reduced costs, improved productivity, and higher quality. To ride this wave of innovation, manufacturing enterprises are changing how product definitions are communicated from paper to models. To achieve industry's vision of the Model Based Enterprise (MBE) the MBE strategy must include model based data interoperability from design to manufacturing and quality in the supply chain. The Model Based Definition (MBD) is created by the original equipment manufacturer (OEM) using Computer Aided Design (CAD) tools. This information is then shared with the supplier so that they can manufacture and inspect the physical parts. Today, suppliers predominantly use Computer Aided Manufacturing (CAM) and Coordinate Measuring Machine (CMM) models for these tasks. Traditionally, the OEM has provided design data to the supplier in the form of two dimensional (2D) drawings, but may also include a three dimensional (3D) shape geometry model, often in a standards based format such as ISO 10303 203:2011 (STEP AP203) The supplier then creates the respective CAM and CMM models and machine programs to produce and inspect the parts. In the MBE vision for model based data exchange, the CAD model must include product and manufacturing information (PMI) in addition to the shape geometry. Today's CAD tools can generate models with embedded PMI. And, with the emergence of STEP AP242, a standards based model with embedded PMI can now be shared downstream. The on going research detailed in this paper seeks to investigate three concepts. First, that the ability to utilize a STEP AP242 model with embedded PMI for CAD to CAM and CAD to CMM data exchange is possible and valuable to the overall goal of a more efficient process. Second, the research identifies gaps in tools, standards, and processes that inhibit industry's ability to cost effectively achieve model based data interoperability in the pursuit of the MBE vision. Finally, it also seeks to explore the interaction between CAD and CMM processes and determine if the concept of feedback from CAM and CMM back to CAD is feasible. The main goal of our study is to test the hypothesis that model based data interoperability from CAD to CAM and CAD to CMM is feasible through standards based integration. This paper presents several barriers to model based data interoperability. Overall, the project team demonstrated the exchange of product definition data between CAD, CAM, and CMM systems using standards based methods. While gaps in standards coverage were identified, the gaps should not stop industry's progress toward MBE. The results of our study provide evidence in support of an open standards method to model based data interoperability, which would provide maximum value and impact to industry.
A Longitudinal Study of the Effects of Child Reported Maternal Warmth on Cortisol Stress Response 15 Years After Parental Divorce
Venue: Psychosomatic medicine

The experience of parental divorce during childhood is associated with an increased risk of behavioral and physical health problems. Alterations in adrenocortical activity may be a mechanism in this relation. Parent child relationships have been linked to cortisol regulation in children exposed to adversity, but prospective research is lacking. We examined maternal warmth in adolescence as a predictor of young adults' cortisol stress response 15 years after parental divorce.  Participants included 240 youth from recently divorced families. Mother and child reports of maternal warmth were assessed at 6 time points across childhood, adolescence, and young adulthood. Offspring salivary cortisol was measured in young adulthood before and after a social stress task. Structural equation modeling was used to predict cortisol response from maternal warmth across early and late adolescence.  Higher child reported maternal warmth in early adolescence predicted higher child reported maternal warmth in late adolescence (standardized regression 0.45, standard error 0.065, p .01) which predicted lower cortisol response to a challenging interpersonal task in young adulthood (standardized regression 0.20, standard error 0.094, p .031) Neither mother reported warmth in early adolescence nor late adolescence was significantly related to offspring cortisol response in young adulthood.  Results suggest that for children from divorced families, a warm mother child relationship after divorce and across development, as perceived by the child, may promote efficient biological regulation later in life.
Unlock big data emotions: Weighted word embeddings for sentiment classification
Venue: 2016 IEEE International Conference on Big Data (Big Data)

Sentiment classification has gained much attention in big data era. Most existing methods rely on bag of words model, which disregard contextual information. In many cases however, the sentiment strength of a word is implicitly associated with its part of speech and context. In this paper, we present a WWE (weighted word embeddings) method that combines word embeddings and part of speech (POS) tagging. First, we used a continuous word representations algorithm (Word2Vec) to train a vector model. The algorithm learns the optimal vectors from the context of surrounding words. According to the cosine similarity between the vector of a word and the vectors of seed words, a polarity score of this word can be calculated. The state of the art SyntaxNet was used for POS tagging. We then computed an overall polarity score of the whole sentence by POS weighted polarity scores of words. At the end, majority voting was applied to determine the final polarity. Our experimental results show that the WWE method is performed with promising outcomes. Additionally, the methodology was demonstrated on the 3 Twitter datasets from different domains. The robustness recommends that this method can be applied on other sentiment classification problems or domains. We also compared the performance on various dimensions of the trained models. A higher dimension achieved a better performance.
Parallel Algorithms for Multirelational Data Mining: Application to Life Science Problems
Venue: Resource Management for Big Data Platforms

Data Mining (DM) algorithms are able to construct models from available data that can be very useful for both business and science. However, a powerful representation language is required to express the highly complex models that stem from structured data. Multirelational algorithms can then take advantage of this representation for both data and models. The drawback is that for very large or highly complex domains multirelational algorithms may require long running times. This problem can be substantially reduced using parallel implementations. In this chapter, we present a survey on parallel approaches to run Inductive Logic Programming (ILP) a flavor of multirelational algorithms. We also analyze different scheduling approaches for those implementations and describe two applications where the proposed approaches may be very useful.
Post growth process for flexible CdS/CdTe thin film solar cells with high specific power.
Venue: Optics express

We demonstrated a flexible CdS/CdTe thin film solar cell with high specific power of approximately 254 W/kg. A flexible and ultra light weight CdS/CdTe cell treated with pre NP etch process exhibited high conversion efficiency of 13.56% in superstrate configuration. Morphological, structural and optical changes of CdS/CdTe thin films were characterized when pre NP etch step was incorporated to the conventional post deposition process. Improvement of photovoltaic parameters can be attributed to the removal of the oxide and the formation of Te rich layer, which benefit the activation process. Pre NP etched cell maintained their flexibility and performance under the repeated tensile strain of 0.13% Our method can pave a way for manufacturing flexible CdS/CdTe thin film solar cells with high specific power for mobile and aerospace applications.
Investigation of relationships between bulk properties and fraction properties of crude oils by application of the intercriteria analysis

Data from assays of 244 crude oils (condensates, extra light, light, intermediate, and heavy crudes) were processed by the InterCriteria Analysis with the aim to investigate the relationships between bulk properties and fraction properties of the crude oils and the degree of similarity between them. It was found that except the crude bulk properties sulfur, Conradson carbon, and metals content all other studied crude bulk properties exhibited lack of statistically meaningful relations or presence of weak statistically meaningful relations with the crude fraction properties. The use of the InterCriteria Analysis showed that crudes with very similar properties could be identified when a large crude database is available. In this way based on a previous experience in oil refining a selection of potentially beneficial new crudes for processing in a refinery could be made.
The TIGGE Project and Its Achievements

AbstractThe International Grand Global Ensemble (TIGGE) was a major component of The Observing System Research and Predictability Experiment (THORPEX) research program, whose aim is to accelerate improvements in forecasting high impact weather. By providing ensemble prediction data from leading operational forecast centers, TIGGE has enhanced collaboration between the research and operational meteorological communities and enabled research studies on a wide range of topics.The paper covers the objective evaluation of the TIGGE data. For a range of forecast parameters, it is shown to be beneficial to combine ensembles from several data providers in a multimodel grand ensemble. Alternative methods to correct systematic errors, including the use of reforecast data, are also discussed.TIGGE data have been used for a range of research studies on predictability and dynamical processes. Tropical cyclones are the most destructive weather systems in the world and are a focus of multimodel ensemble research.
Indian Stock Market Volatility Asymmetric GARCH Models

Over the last few years, modeling and forecasting volatility of a financial time series has become a fertile area for research. This is simply because of the fact that volatility is considered as an important concept for many economic and financial applications. The examples may refer to the portfolio optimization, risk management and asset pricing etc. This study models the volatility present in the inter day returns in the stock of the two major national indices of India. Sensitive Index or S&P BSE SENSEX related to Bombay Stock Exchange (BSE) and S&P CNX NIFTY associated with National Stock Exchange (NSE) The focus is on daily stock return data corresponding to the 1990 2013 time intervals. The objective is to model the phenomena of volatility clustering and persistence of leverage effect in empirical time series using asymmetric GARCH family of models. Research showed that GJR GARCH model successfully models both the S&P BSE SENSEX and S&P CNX NIFTY returns data.
Enhancing portfolio returns by identifying high growth companies in Indian stock market using artificial intelligence
Venue: 2016 IEEE International Conference on Recent Trends in Electronics, Information Communication Technology (RTEICT)

The Indian Stock market, with over 5000 listed companies, where sorting of companies requires analysis of numerous financial parameters and ratios, is an example of a database from which single sub set or group of companies need to be found out for investing. With growing popularity of artificial intelligence in stock trading, it has been applied heavily into stock market for picking stocks. We apply K means clustering algorithm, which uses unsupervised learning for clustering a dataset, to the listed Indian companies in an attempt to isolate businesses showing exceptionally high growth. The results show that, it is possible to achieve such grouping in an efficient and timely manner by K means clustering. Testing of results is done by comparing absolute price returns and risk adjusted returns of the obtained cluster and that of the popular and relevant market indices and top performing funds. The returns obtained from the cluster obtained using K means clustering algorithm are found to outperform the indices and mutual funds by a comprehensive margin. These results emphasizes the role played by growth in stock performance in emerging markets along with subsequent conclusions arrived at therein, have important implications in the field of stock selection and portfolio management.
50 years of biometric research: Accomplishments, challenges, and opportunities
Venue: Pattern Recognit. Lett.

Biometric recognition refers to the automated recognition of individuals based on their biological and behavioral characteristics such as fingerprint, face, iris, and voice. The first scientific paper on automated fingerprint matching was published by Mitchell Trauring in the journal Nature in 1963. The first objective of this paper is to document the significant progress that has been achieved in the field of biometric recognition in the past 50 years since Trauring's landmark paper. This progress has enabled current state of the art biometric systems to accurately recognize individuals based on biometric trait(s) acquired under controlled environmental conditions from cooperative users. Despite this progress, a number of challenging issues continue to inhibit the full potential of biometrics to automatically recognize humans. The second objective of this paper is to enlist such challenges, analyze the solutions proposed to overcome them, and highlight the research opportunities in this field. One of the foremost challenges is the design of robust algorithms for representing and matching biometric samples obtained from uncooperative subjects under unconstrained environmental conditions (e.g. recognizing faces in a crowd) In addition, fundamental questions such as the distinctiveness and persistence of biometric traits need greater attention. Problems related to the security of biometric data and robustness of the biometric system against spoofing and obfuscation attacks, also remain unsolved. Finally, larger system level issues like usability, user privacy concerns, integration with the end application, and return on investment have not been adequately addressed. Unlocking the full potential of biometrics through inter disciplinary research in the above areas will not only lead to widespread adoption of this promising technology, but will also result in wider user acceptance and societal impact.
Discriminant Correlation Analysis: Real Time Feature Level Fusion for Multimodal Biometric Recognition
Venue: IEEE Transactions on Information Forensics and Security

Information fusion is a key step in multimodal biometric systems. The fusion of information can occur at different levels of a recognition system, i.e. at the feature level, matching score level, or decision level. However, feature level fusion is believed to be more effective owing to the fact that a feature set contains richer information about the input biometric data than the matching score or the output decision of a classifier. The goal of feature fusion for recognition is to combine relevant information from two or more feature vectors into a single one with more discriminative power than any of the input feature vectors. In pattern recognition problems, we are also interested in separating the classes. In this paper, we present discriminant correlation analysis (DCA) a feature level fusion technique that incorporates the class associations into the correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pairwise correlations across the two feature sets and, at the same time, eliminating the between class correlations and restricting the correlations to be within the classes. Our proposed method can be used in pattern recognition applications for fusing the features extracted from multiple modalities or combining different feature vectors extracted from a single modality. It is noteworthy that DCA is the first technique that considers class structure in feature fusion. Moreover, it has a very low computational complexity and it can be employed in real time applications. Multiple sets of experiments performed on various biometric databases and using different feature extraction techniques, show the effectiveness of our proposed method, which outperforms other state of the art approaches.
The Legal Maxims of Islamic Law (Excluding Five Leading Legal Maxims) and Their Applications in Islamic Finance

Al qawa'id al fiqhiyah or legal maxims of Islamic Law are the general rules of fiqh that portray the goals and objectives of the Shari'ah. They are applied in various cases that come under the common rulings and play a very important role in deducing many rules of fiqh since they provide a guideline to come up with particular hukm. There are five leading maxims, as reflected in the Majallah, such as, al umur bi maqasidiha (matters are determined according to intentions) al yaqin la yazul bi alshakk (certainty is not overruled by doubt) al mashaqqah tajlib al taysir (hardship begets facility) al darar yuzal (harm must be eliminated) and al 'adah muhakkamah (custom is a basis for judgment) Other than these five leading maxims, which are applicable to a number of legal principles, there are other maxims, which are not as extensive as the main maxims, but nonetheless, address a number of more detailed issues in fiqh. Usually, the maxims incorporated in this category are either an extension of maxims derived from the five leading maxims or might be unrelated. This paper seeks to analyse those maxims, other than the leading maxims, which are related to Islamic finance and to understand their roles and applications in the Islamic banking and finance industry.
Effects of Physical Exercise on Health and Well Being of Individuals Living With a Dementia in Nursing Homes: A Systematic Review.
Venue: Journal of the American Medical Directors Association

Physical exercise interventions have benefits for older individuals and improve the health and well being of individuals living with a dementia, specifically those living in nursing homes.  Report evidence from randomized controlled trials and cluster randomized control trials that evaluated the effects of physical exercise interventions on individuals living with a dementia in nursing homes.  Web of Science, Scopus, Science Direct, Academic Search Complete, Proquest Central, British Medical Journal Database, PubMed, Cochrane Library, PEDro, Informit, Informa, and Nursing Consult were searched for relevant clinical trials and snowballing of recommended studies.  One reviewer screened articles on inclusion criteria and identified relevant studies.  Data extraction was performed by 1 reviewer and checked by second and third reviewers. Two authors assessed the methodological quality and risk of bias of the relevant studies.  Twelve study populations consisting of individuals living with a dementia in nursing homes were included (n 901) Different types of physical exercises were undertaken: multimodal (n 6) walking (n 5) music and movement (n 2) and hand exercises (n 1) The parameters of the interventions varied across the studies. Most of the studies reported significant positive effects of physical exercise on cognition, agitation, mood, mobility, and functional ability for individuals living with dementia in nursing homes.  The main limitations were the heterogeneity of design, small samples, and short interventions.  There is emerging evidence that physical exercise significantly benefits individuals living with a dementia in nursing homes. Higher quality research is required adopting more rigorous methods, including longer interventions and larger samples to determine optimum parameters of the physical exercise interventions evaluated.
Exercise is more than medicine: The working age population's well being and productivity
Venue: Journal of sport and health science

Physical activity (PA) includes muscle activity during exercise, manual work, and leisure time activities including sport. Conflicting results exist regarding health effects of PA that may deteriorate with manual work and elite sports, but improve when performed in moderation in accordance with international guidelines and may additionally enhance well being and productivity.  In Denmark 15 randomized controlled trials have been conducted, introducing exercise at the workplace enrolling >3500 workers. The interventions lasted from 10 to 52 weeks and offered ~1 h weekly supervised exercise during working hours according to the concept of intelligent physical exercise training (IPET) that is based on evidenced sports sciences training principles and tailored to work exposure, employee health status, and physical capacity. Questionnaire surveys and health checks including blood and muscle sampling were performed at baseline and follow up. The job groups included: office and computer workers, dentists, industrial technicians, cleaning personnel, health care workers, construction workers, and fighter/helicopter pilots.  In all job groups significant improvements were documented regarding health outcomes. These were job group specific: neck pain was reduced among office and computer workers, dentists, industrial laboratory technicians, health care workers as well as fighter pilots. Cardio respiratory fitness a health risk indicator for cardio metabolic diseases was improved among office and computer workers, health care workers, and construction workers. Additionally, other improvements were evidenced such as increased muscle strength and balance control. Importantly, productivity increased with improved muscle strength and decreased body mass index.  IPET does enhance health if an exercise program with evidenced efficacy is implemented by expert trainees with support of the employer. Accordingly, in every study group outcomes of improved health were documented and the effect sizes were of clinical relevance. Cost effectiveness estimates indicate acceptable cost relative to savings on health expenses and lost productivity.
Metro Manila through the gentrification lens: Disparities in urban planning and displacement risks

Using Metro Manila as a case study, this paper investigates whether and how its current urban form and pattern of transformation can be explained by the theories of gentrification developed within the cities of the Global North. Two core features of gentrification are examined: the production of exclusive spaces and the related displacement of the poor or low income households. The uneven development across the metropolis is discussed in terms of the longstanding tension between the weak city state and the dominance of the landed elites. Falling through the gap are the urban poor, who arrange their housing within informal settlements and are exposed to multiple vulnerabilities, including the risk of displacement. Drawing on an empirical study of the displacement impacts of a public transportation project, this paper provides useful insights into how gentrification induced displacement may unfold if land based development takes place in a densely populated metropolis such as Metro Manila.