nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic Simulation and Optimum Operation Strategy of a Trigeneration System Serving a Hospital

This paper presents a numerical analysis of a trigeneration system in a hospital, aiming at determining the cost optimal operating strategy as a function of the energy demands to be matched. The system includes: A natural gas fired reciprocating engine, heat exchangers for waste heat recovery, a single stage LiBr H2O Absorption Chiller (ACH) a cooling tower, pumps, a backup boiler, a backup vapour compression electric chiller, storage tanks, valves, mixers. For such system, a dynamic simulation model was developed in TRNSYS environment; the model includes detailed algorithms for all the components of the system. A case study was developed, referred to a hospital application, in which a Combined Heat, Cooling and Power (CHCP) system provides electricity, thermal and cooling energy. The electric energy demand was obtained by using real measured data and calibrating hospital literature data, whereas the demand for heating and cooling was estimated by means of a detailed simulation model. A detailed economic analysis was also included in the model, aiming at investigating the optimal control strategy needed to maximize the overall thermo economic performance of the system. To this scope, different control strategies were analysed. The most conventional operating strategy, Thermal Load Tracking mode (TLT) was compared with two alternative strategies: The Maximum Power Thermal Load Tracking mode (MPTLT) and the Electricity Load Tracking mode (ELT) MPTLT is a strategy featured by a thermal load tracking mode, but the engine, differently from TLT one, operates always at maximum power. ELT is a strategy in which the power provided by the engine is always less or equal to the electrical demand. In the paper, the results of the case study are presented on different time bases (days, weeks, years) Such results show that the ELT control strategy can achieve a better profitability, with a simple pay back period, SPB, equal to 4 years. The conventional strategy (TLT) is shown to be the worst from the economic point of view, but among the best as for energy saving potential.
A new approach to session identification by applying fuzzy c means clustering on web logs
Venue: 2016 IEEE Symposium Series on Computational Intelligence (SSCI)

In this paper a new algorithm for session identification in web logs is outlined, based on the fuzzy c means clustering of the available data. The novelty of the proposed methodology lies in the initialization of the partition matrix using subtractive clustering, the examination of the effect a variety of distance metrics have on the clustering process (in addition to the widely used Euclidean distance) the determination of the number of user sessions based on candidate sessions and the representation of the session data. The experimental results show that the proposed methodology is effective in the reconstruction of user sessions and can distinguish individual sessions more accurately than baseline time heuristic methods proposed in literature.
Privacy Preserving Outsourced Association Rule Mining on Vertically Partitioned Databases
Venue: IEEE Transactions on Information Forensics and Security

Association rule mining and frequent itemset mining are two popular and widely studied data analysis techniques for a range of applications. In this paper, we focus on privacy preserving mining on vertically partitioned databases. In such a scenario, data owners wish to learn the association rules or frequent itemsets from a collective data set and disclose as little information about their (sensitive) raw data as possible to other data owners and third parties. To ensure data privacy, we design an efficient homomorphic encryption scheme and a secure comparison scheme. We then propose a cloud aided frequent itemset mining solution, which is used to build an association rule mining solution. Our solutions are designed for outsourced databases that allow multiple data owners to efficiently share their data securely without compromising on data privacy. Our solutions leak less information about the raw data than most existing solutions. In comparison to the only known solution achieving a similar privacy level as our proposed solutions, the performance of our proposed solutions is three to five orders of magnitude higher. Based on our experiment findings using different parameters and data sets, we demonstrate that the run time in each of our solutions is only one order higher than that in the best non privacy preserving data mining algorithms. Since both data and computing work are outsourced to the cloud servers, the resource consumption at the data owner end is very low.
Market Basket Analysis: Identify the Changing Trends of Market Data Using Association Rule Mining

also known as association rule learning or affinity analysis, is a data mining technique that can be used in various fields, such as marketing, bioinformatics, education field, nuclear science etc. The main aim of MBA in marketing is to provide the information to the retailer to understand the purchase behavior of the buyer, which can help the retailer in correct decision making. There are various algorithms are available for performing MBA. The existing algorithms work on static data and they do not capture changes in data with time. But proposed algorithm not only mine static data but also provides a new way to take into account changes happening in data. This paper discusses the data mining technique i.e. association rule mining and provide a new algorithm which may helpful to examine the customer behaviour and assists in increasing the sales.
Soft set based association rule mining
Venue: Knowl. Based Syst.

Detailed insight into soft set based association rule mining is provided.Drawbacks of some existing definitions are pointed out and rectified.New concepts and algorithms for soft association rule mining are developed.Essentials of new concepts and algorithms are highlighted by two case studies. Association rules, one of the most useful constructs in data mining, can be exerted to capture interesting dependencies between variables in large datasets. Herawan and Deris initiated the investigation of mining association rules from transactional datasets using soft set theory. Unfortunately, some existing concepts in the literature were unable to realize properly Herawan and Deris's initial idea. This paper aims to offer further detailed insights into soft set based association rule mining. With regard to regular association rule mining using soft sets, we refine several existing concepts to improve the generality and clarity of former definitions. Regarding maximal association rule mining based on soft sets, we point out the drawbacks of some existing definitions and offer some way to rectify the problem. A number of new notions, such as transactional data soft sets, parameter taxonomic soft sets, parameter cosets, realizations and M realizations of parameter sets are proposed to facilitate soft set based association rule mining. Several algorithms are designed to find M realizations of parameter sets or extract z M strong and g M reliable maximal association rules in parameter taxonomic soft sets. We also present an example to illustrate potential applications of our method in clinical diagnosis. Moreover, two case studies are conducted to highlight the essentials of soft set based association rule mining approach.
Intelligent phishing url detection using association rule mining
Venue: Human centric Computing and Information Sciences

Phishing is an online criminal act that occurs when a malicious webpage impersonates as legitimate webpage so as to acquire sensitive information from the user. Phishing attack continues to pose a serious risk for web users and annoying threat within the field of electronic commerce. This paper focuses on discerning the significant features that discriminate between legitimate and phishing URLs. These features are then subjected to associative rule mining apriori and predictive apriori. The rules obtained are interpreted to emphasize the features that are more prevalent in phishing URLs. Analyzing the knowledge accessible on phishing URL and considering confidence as an indicator, the features like transport layer security, unavailability of the top level domain in the URL and keyword within the path portion of the URL were found to be sensible indicators for phishing URL. In addition to this number of slashes in the URL, dot in the host portion of the URL and length of the URL are also the key factors for phishing URL.
From Observational Studies to Causal Rule Mining
Venue: ACM Trans. Intell. Syst. Technol.

Randomised controlled trials (RCTs) are the most effective approach to causal discovery, but in many circumstances it is impossible to conduct RCTs. Therefore, observational studies based on passively observed data are widely accepted as an alternative to RCTs. However, in observational studies, prior knowledge is required to generate the hypotheses about the cause effect relationships to be tested, and hence they can only be applied to problems with available domain knowledge and a handful of variables. In practice, many datasets are of high dimensionality, which leaves observational studies out of the opportunities for causal discovery from such a wealth of data sources. In another direction, many efficient data mining methods have been developed to identify associations among variables in large datasets. The problem is that causal relationships imply associations, but the reverse is not always true. However, we can see the synergy between the two paradigms here. Specifically, association rule mining can be used to deal with the high dimensionality problem, whereas observational studies can be utilised to eliminate noncausal associations. In this article, we propose the concept of causal rules (CRs) and develop an algorithm for mining CRs in large datasets. We use the idea of retrospective cohort studies to detect CRs based on the results of association rule mining. Experiments with both synthetic and real world datasets have demonstrated the effectiveness and efficiency of CR mining. In comparison with the commonly used causal discovery methods, the proposed approach generally is faster and has better or competitive performance in finding correct or sensible causes. It is also capable of finding a cause consisting of multiple variables a feature that other causal discovery methods do not possess.
Relationship between anxiety and medical disorders among compulsory military service candidates between the years 1998 2013
Venue: Psychiatry Research

One of the most common psychiatric diagnoses among adolescents is anxiety disorder. Many of the anxiety symptoms are expressed physiologically, and therefore can mimic other medical conditions. The aim of this study was to examine the association between anxiety disorders and other medical conditions sharing common symptoms with anxiety (MDSCSA: Irritable Bowel Syndrome, asthma, migraine and hyperhidrosis) The study was based on the national database of the candidates for military service in Israel. Data for the years 1998 2013 was retrieved to create the study dataset. The final cohort population was comprised of 1,229,461 military service candidates. Anxiety prevalence and its association with other medical conditions sharing the same symptoms was examined in the cohort. The results showed significant statistical association between anxiety and IBS, asthma, migraine and hyperhidrosis. These findings support the fact that there is a clear association between anxiety disorder and the examined medical conditions. Moreover, in the military setting, the primary care physician has an important role in giving a correct diagnosis for soldiers presenting with symptoms that can be regarded both to anxiety and to other physical illnesses.
The Relationship Between Generalized Anxiety Symptoms and Treatment Dropout Among Women in Residential Treatment for Substance Use Disorders
Venue: Substance use misuse

Generalized anxiety disorder (GAD) co occurs with substance use disorders (SUDs) at an alarmingly high rate, and the presence of anxiety is associated with an increased risk for relapse to substance use following treatment. Furthermore, comorbid SUDs and other forms of psychopathology are associated with an increased risk for leaving treatment against medical advice (AMA)  Research has yet to examine whether the presence of GAD symptoms is associated with leaving treatment AMA in SUDs populations. Thus, the current study sought to address this important gap in the literature by examining this relationship among a sample of 122 women in residential treatment for SUDs.  Results demonstrated that GAD symptoms were significantly associated with the decision to leave treatment AMA after controlling for age, education, problematic alcohol and drug use, and depression symptoms.  Our finding indicates the potential importance of assessing and targeting GAD in treatment for SUDs, which may increase treatment compliance.
The association of an inability to form and maintain close relationships due to a medical condition with anxiety and depressive disorders.
Venue: Journal of affective disorders

While low social support is a risk factor for mental illness, anxiety and depression's relationship with social impairment specifically resulting from a medical condition is poorly understood. We hypothesize that when a medical illness makes it difficult for people to form and maintain close relationships with others, they will be at increased risk for anxiety and depression.  Two nationally representative surveys, the National Comorbidity Survey Replication and National Latino and Asian American Study, included 6805 adults with at least one medical illness and information on social impairment attributed to a medical condition. The Composite International Diagnostic Interview evaluated a 12 month history of anxiety and depressive disorders.  8.2% of our sample had at least moderate difficulty in forming and maintaining close relationships due to a medical condition. In bivariate analyses, younger age, Latino ethnicity, less education, worse financial status, more chronic illnesses, physical health and discomfort, and problems with mobility, home management, and self care were associated with this social impairment. In multivariable analyses accounting for possible confounders, there was a dose dependent relationship between social impairment and the prevalence of anxiety and depression.  Data are cross sectional and our analyses are therefore unable to determine cause and effect relationships.  Among adults with one or more medical conditions, social impairment attributed to medical illness was associated with a significantly greater odds of anxiety and depression. Further clarification of this relationship could inform more targeted, personalized interventions to prevent and/or alleviate mental illness in those with chronic medical conditions.
Automatically Learning Semantic Features for Defect Prediction
Venue: 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE)

Software defect prediction, which predicts defective code regions, can help developers find bugs and prioritize their testing efforts. To build accurate prediction models, previous studies focus on manually designing features that encode the characteristics of programs and exploring different machine learning algorithms. Existing traditional features often fail to capture the semantic differences of programs, and such a capability is needed for building accurate prediction models. To bridge the gap between programs' semantics and defect prediction features, this paper proposes to leverage a powerful representation learning algorithm, deep learning, to learn semantic representation of programs automatically from source code. Specifically, we leverage Deep Belief Network (DBN) to automatically learn semantic features from token vectors extracted from programs' Abstract Syntax Trees (ASTs) Our evaluation on ten open source projects shows that our automatically learned semantic features significantly improve both within project defect prediction (WPDP) and cross project defect prediction (CPDP) compared to traditional features. Our semantic features improve WPDP on average by 14.7% in precision, 11.5% in recall, and 14.2% in F1. For CPDP, our semantic features based approach outperforms the state of the art technique TCA+ with traditional features by 8.9% in F1.
Dynamic Bayesian Network Based Anomaly Detection for In Process Visual Inspection of Laser Surface Heat Treatment
Venue: ML4CPS

We present the application of a cyber physical system for inprocess quality control based on the visual inspection of a laser surface heat treatment process. To do this, we propose a classification framework that detects anomalies in recorded video sequences that have been preprocessed using a clustering based method for feature subset selection. One peculiarity of the classification task is that there are no examples with errors, since major irregularities seldom occur in efficient industrial processes. Additionally, the parts to be processed are expensive so the sample size is small. The proposed framework uses anomaly detection, cross validation and sampling techniques to deal with these issues. Regarding anomaly detection, dynamic Bayesian networks (DBNs) are used to represent the temporal characteristics of the normal process. Experiments are conducted with two different types of DBN structure learning algorithms, and classification performance is assessed on both anomaly free examples and sequences with anomalies simulated by experts.
Dynamic Bayesian network based anomaly detection for in process visual inspection of laser surface heat treatment

We present the application of a cyber physical system for inprocess quality control based on the visual inspection of a laser surface heat treatment process. To do this, we propose a classification framework that detects anomalies in recorded video sequences that have been preprocessed using a clustering based method for feature subset selection. One peculiarity of the classification task is that there are no examples with errors, since major irregularities seldom occur in efficient industrial processes. Additionally, the parts to be processed are expensive so the sample size is small. The proposed framework uses anomaly detection, cross validation and sampling techniques to deal with these issues. Regarding anomaly detection, dynamic Bayesian networks (DBNs) are used to represent the temporal characteristics of the normal process. Experiments are conducted with two diferent types of DBN structure learning algorithms, and classification performance is assessed on both anomalyfree examples and sequences with anomalies simulated by experts.
Psychometrics of shared decision making and communication as patient centered measures for two language groups.
Venue: Psychological assessment

Shared decision making (SDM) and effective patient provider communication are key and interrelated elements of patient centered care that impact health and behavioral health outcomes. Measurement of SDM and communication from the patient's perspective is necessary in order to ensure that health care systems and individual providers are responsive to patient views. However, there is a void of research addressing the psychometric properties of these measures with diverse patients, including non English speakers, and in the context of behavioral health encounters. This study evaluated the psychometric properties of 2 patient centered outcome measures, the Shared Decision Making Questionnaire 9 (SDM Q) and the Kim Alliance Scale Communication subscale (KAS CM) in a sample of 239 English and Spanish speaking behavioral health patients. One dominant factor was found for each scale and this structure was used to examine whether there was measurement invariance across the 2 language groups. One SDM Q item was inconsistent with the configural invariance comparison and was removed. The remaining SDM Q items exhibited strong invariance, meaning that item loadings and item means were similar across the 2 groups. The KAS CM items had limited variability, with most respondents indicating high communication levels, and the invariance analysis was done on binary versions of the items. These had metric invariance (loadings the same over groups) but several items violated the strong invariance test. In both groups, the SDM Q had high internal consistency, whereas the KAS CM was only adequate. These findings help interpret results for individual patients, taking into account cultural and linguistic differences in how patients perceive SDM and patient provider communication.
Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource
Venue: LREC

Word embeddings have recently seen a strong increase in interest as a result of strong performance gains on a variety of tasks. However, most of this research also underlined the importance of benchmark datasets, and the difficulty of constructing these for a variety of language specific tasks. Still, many of the datasets used in these tasks could prove to be fruitful linguistic resources, allowing for unique observations into language use and variability. In this paper we demonstrate the performance of multiple types of embeddings, created with both count and prediction based architectures on a variety of corpora, in two language specific tasks: relation evaluation, and dialect identification. For the latter, we compare unsupervised methods with a traditional, hand crafted dictionary. With this research, we provide the embeddings themselves, the relation evaluation task benchmark for use in further research, and demonstrate how the benchmarked embeddings prove a useful unsupervised linguistic resource, effectively used in a downstream task.
Effect of Energy Polydispersity on the Nature of Lennard Jones Liquids.
Venue: The journal of physical chemistry. B

In the companion paper Ingebrigtsen T. S.  liquids, which represent most molecular liquids without hydrogen bonds, was studied. More specifically, it was shown that even highly size polydisperse LJ liquids are Roskilde simple (RS) liquids. RS liquids are liquids with strong correlation between constant volume equilibrium fluctuations of virial and potential energy and are simpler than other types of liquids. Moreover, it was shown that size polydisperse LJ liquids have isomorphs to a good approximation. Isomorphs are curves in the phase diagram of RS liquids along which structure, dynamics, and some thermodynamic quantities are invariant in dimensionless (reduced) units. In this paper, we study the effect of energy polydispersity on the nature of LJ liquids. We show that energy polydisperse LJ liquids are RS liquids. However, a tendency of particle segregation, which increases with the degree of polydispersity, leads to a loss of strong virial potential energy correlation but is mitigated by increasing temperature and/or density. Isomorphs are a good approximation also for energy polydisperse LJ liquids, although particle resolved quantities display a somewhat poorer scaling compared to the mean quantities along the isomorph.
New Insights into the Epithelial to Mesenchymal Transition in Cancer.
Venue: Trends in pharmacological sciences

The role of the epithelial to mesenchymal transition (EMT) in cancer progression is a long debated issue. Recent evidence shows that EMT is not a prerequisite for cancer metastasis, but does confer chemoresistance. Future studies are needed to profile EMT events in different cancer types and under different circumstances to explore their potentials as therapeutic targets.