nils-herrmann/clean_scirepeval huggingface dataset:
A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy
Venue: Appl. Soft Comput.

The proposed method uses a local search technique which is embedded in particle swarm optimization (PSO) to select the reduced sized and salient feature subset. The goal of the local search technique is to guide the PSO search process to select distinct features by using their correlation information. Therefore, the proposed method selects the subset of features with reduced redundancy. A hybrid feature selection method based on particle swarm optimization is proposed.Our method uses a novel local search to enhance the search process near global optima.The method efficiently finds the discriminative features with reduced correlations.The size of final feature set is determined using a subset size detection scheme.Our method is compared with well known and state of the art feature selection methods. Feature selection has been widely used in data mining and machine learning tasks to make a model with a small number of features which improves the classifier's accuracy. In this paper, a novel hybrid feature selection algorithm based on particle swarm optimization is proposed. The proposed method called HPSO LS uses a local search strategy which is embedded in the particle swarm optimization to select the less correlated and salient feature subset. The goal of the local search technique is to guide the search process of the particle swarm optimization to select distinct features by considering their correlation information. Moreover, the proposed method utilizes a subset size determination scheme to select a subset of features with reduced size. The performance of the proposed method has been evaluated on 13 benchmark classification problems and compared with five state of the art feature selection methods. Moreover, HPSO LS has been compared with four well known filter based methods including information gain, term variance, fisher score and mRMR and five well known wrapper based methods including genetic algorithm, particle swarm optimization, simulated annealing and ant colony optimization. The results demonstrated that the proposed method improves the classification accuracy compared with those of the filter based and wrapper based feature selection methods. Furthermore, several performed statistical tests show that the proposed method's superiority over the other methods is statistically significant.
IPO Trading with Short term Intraday Temporal Functionalities

Trading is a temporal (i.e. time based) historical living system with a number of functions, like: Initial Public Offerings (IPO) Seasoned Equity Offerings (SEO) stock (instrument) price action Gaps, Breakouts, etc. In this domain, a number of warning dynamics timing functionalities is available, like: On Open Gup Ups (ooGUp) On Open Gup Downs (ooGDn) Morning Breakouts (mB) etc. All these time based functionalities are regarded as 2nd level functions (i.e. functions of functions; because of the timing involved) with great trading opportunities, and they are defined for the first time in the corporate finance literature by this paper as Temporal (timing) Trading Functionalities (TTF) In particular, the IPOs with the embedded TTF functionalities are great trading opportunities for the institutions, the individual (noncommercial) market investors, the swing traders, and the speculators. Data analysis shows that during the seasoned equity offerings time, shareowners significantly increase their share share holding, including offerings that would be classified as overpriced at that time; hence, the involved trading volatility is increased resulting in great trading and profit opportunities. This paper contributes to corporate finance literature by examining the IPOs functions and define and document their inherit TTF functionalities. For this purpose, four categories of shareholders are regarded: The longterm institution and non commercial traders (investors) the swing momentary institution traders (institutions) the shortterm non commercial traders (speculators) and the intraday non commercial traders (speculators) Paper concludes that, in IPO/TTF trading, the swing traders(institutions) incorporating in their trading strategies the short term TTF functionalities, are benefit at the expense of momentary and intraday speculators, while the long term investors are not affected by the IPO offerings.
Seasoned Equity Offerings as Technical Market Anomalies: Long Term Temporal Trading Functionalities

The main goal of this paper is to approach the Seasoned Equity Offerings (SEO) trading opportunities as technical market anomalies and under the prism of a number of temporal (time based) long term trading functionalities (long term TTF) introduced for the first time in corporate finance literature. The long term is defined, for the purposes of this paper, as the 3 year time period, traded usually with daily, weekly and monthly time frames. Trading is a temporal (i.e. time based) historical living system with a number of functions, like: SEO, IPO, stock (instrument) price action Gaps, Breakouts, etc. In this domain, a number of warning long term and short term dynamics timing functionalities is available, like: candlestick patterns breaks, price action patterns pivotal lines breaks, on open gup ups (ooGUp) on open gup downs (ooGDn) morning breakouts (mB) etc. All these time based functionalities are regarded as 2nd level functions (i.e. functions of functions; because of the timing involved) with great trading opportunities, and they are defined "Cfor the first time in the corporate finance literature by this paper as temporal (timing) trading functionalities. In particular, the SEOs with the embedded long term TTF functionalities are great trading opportunities for the institutions, the individual (non commercial) market investors, the swing traders, and the speculators. Data analysis shows that during the seasoned equity offerings time, shareowners significantly increase their share holding, including offerings that would be classified as overpriced at that time; hence, the involved trading volatility is increased resulting in great trading and profit opportunities. This paper contributes to corporate finance literature by examining the SEOs functions and define and document their inherit TTF functionalities. For this purpose, four categories of share holders are regarded: The long term institution non commercial traders (investors) the swing momentary institution traders (institutions) the short term non commercial traders (speculators) and the intraday non commercial traders (speculators) Paper concludes that, in SEO/long term TTF trading, apart from the insiders, the swing traders (usually the smart money and the institutions) are more benefited, at the expense of momentary short term and intraday speculators, while the long term investors are not affected by the SEO offerings.
Personalized Temporal Trading Functionalities Engaged in CalendarMarket Anomalies: Empirical Evidences from the 2007 and 2009Financial Crises

In volatile markets like the calendar market anomalies institutions, traders and speculators constantly search for predictable security's price action patterns; and their trading plans disturb security prices when they attempt to exploit the uprising trading opportunities. Generic forecasting patterns once become known to public are, therefore, unlikely to win for long periods of time and they will self destruct when discovered by a large number of traders and speculators. Therefore, this gives rise to non stationarities in the time series of financial returns and complicates formal tests and evidences of market efficiency, market anomalies and the search for successful and profitable forecasting approaches. Obviously, the need for adapted (i.e. no generic) strategies in trading the calendar market anomalies is apparent. This personalization is complicated and is referred to particular calendar market anomaly (seasonality) to trading instrument characteristics (e.g. beta functionality) and to the traders' profile (investors, swing traders, intraday speculators, etc. Hence, in the calendar market anomalies domain, the personalized (adaptive) temporal dynamic approaches could improve trading returns by incorporating temporal trading functionalities. The main target of this paper is to investigate, analyze and document the personalized trading functionalities enclosed in calendar market anomalies. These functionalities are characterized as temporal because of the "time" function involved by default in calendar market anomalies. In particular this article is focused on the identification of monthly calendar anomalies on the Greek and Bulgarian stock markets. Empirical evidences, before and after the 2007 and 2009 financial crises, are presented in support of the hypothesis that small and less liquid markets tend to exhibit market anomalies. In the case of Greece these anomalies are so strong that they are not influenced by the recent crises. Empirical evidences also show that crises may change the long run calendar anomalies trading functionalities and therefore lead to an improved Market efficiency as a short run effect. Paper's innovation is related to the introduction and discussion of the personalized (adapted) temporal trading functionalities encapsulated in markets inefficiency, as far as the calendar markets anomalies is concern. Actually, the presented analysis, in this article, enhances the existing portfolio of approaches and knowledge about the impact of financial crises on real firm behavior during the calendar market anomalies periods.
Effective electrostatic interactions among charged thermo responsive microgels immersed in a simple electrolyte.
Venue: The Journal of chemical physics

This work explores the nature and thermodynamic behavior of the effective electrostatic interactions among charged microgels immersed in a simple electrolyte, taking special interest in the effects due to the thermally induced variation of the microgel size while the remaining parameters (microgel charge and concentration, plus the amount of added salt) are kept constant. To this end, the rigorous approach obtained from applying the precise methodology of the dressed ion theory to the proper definition of the effective direct correlation functions, which emerge from tracing out the degrees of freedom of the microscopic ions, is employed to provide an exact description of the parameters characterizing such interactions: screening length, effective permittivity, and renormalized charges. A model solution with three components is assumed: large permeable anionic spheres for the microgels, plus small charged hard spheres of equal size for the monovalent cations and anions. The two body correlations among the components of this model suspension, used as the input for the determination of the effective interaction parameters, are here calculated by using the hyper netted chain approximation. It is then found that at finite microgel concentrations the values of these parameters change as the microgel size increases, even though the ionic strength of the supporting electrolyte and the bare charge of the microgels remain fixed during this process. The variation of the screening length, as well as that of the effective permittivity, is rather small, but still interesting in view of the fact that the corresponding Debye length stays constant. The renormalized charges, in contrast, increase markedly as the microgels swell. The ratio of the renormalized charge to the corresponding analytic result obtained in the context of an extended linear response theory allows us to introduce an effective charge that accounts for the non linear effects induced by the short ranged association of microions to the microgels. The behavior of these effective charges as a function of the amount of added salt and the macroion charge, size, and concentration reveals the interplay among all these system parameters.
Meta models in Computer Experiments: Kriging versus Artificial Neural Networks
Venue: Qual. Reliab. Eng. Int.

Non stochastic simulation models, such as finite element or computational fluid dynamics, often support real experiments in industrial research. It has become a common practice to provide a meta model as computer experiments can be highly complex and time consuming, and the design space is often broad. The meta model is an approximation of the computer experiments response adapted both globally and locally on the design space, in order to capture local minima/maxima. The Kriging model, first proposed in Geostatistics, is doubtlessly the most popular meta model because of its recognized ability to provide high quality predictions. The underlying correlation structure can be evaluated either by estimating the parameters of correlation or by means of a variogram. In this paper, the performance of the Kriging model is compared with an Artificial Neural Network meta model in order to determine which model guarantees higher accuracy in predicting the result of four dimensional com putational fluid dynamics experiments for low pressure turbines where energy loss values are provided
A COMPARISON OF ARTIFICIAL NEURAL NETWORKS AND ORDINARY KRIGING DEPTH MAPS OF THE LOWER AND UPPER PANNONIAN STAGE BORDER IN THE BJELOVAR SUBDEPRESSION, NORTHERN CROATIA

Computerised mapping of subsurface strata is possible with a wide range of methods and techniques, such as geostatistical interpolation and stochastic simulations, but also with geomathematical methods. Geomathematical methods are, for example, the use of statistics in geology and the use of artificial neural networks. Artificial neural networks are primarily used in the case of flawed data and data that is in a non linear relation. The set hypothesis of successful mapping of depth data using this original artificial neural network algorithm is confirmed using statistical analysis and comparison with geostatistical interpolation methods. The algorithm is made in ,R" an open source statistical computing software, and is used on the mapping of depth of the e log marker ,Rs5" in the Bjelovar Subdepression, Northern Croatia, that is the border between the Lower and Upper Pannonian stages in the Croatian part of the Pannonian Basin System. The neural network architecture that produced the best responses is a network with two hidden layers, with 10 and 6 neurons, respectively. A backpropagation algorithm is used. Two methods were compared by cross validation and the neural network produced a mean squared error as 16294.5, and Ordinary Kriging produced 14638.35.
Modeling of surface dust concentrations using neural networks and kriging

Creating models which are able to accurately predict the distribution of pollutants based on a limited set of input data is an important task in environmental studies. In the paper two neural approaches: (multilayer perceptron (MLP) and generalized regression neural network (GRNN) and two geostatistical approaches: (kriging and cokriging) are using for modeling and forecasting of dust concentrations in snow cover. The area of study is under the influence of dust emissions from a copper quarry and a several industrial companies. The comparison of two mentioned approaches is conducted. Three indices are used as the indicators of the models accuracy: the mean absolute error (MAE) root mean square error (RMSE) and relative root mean square error (RRMSE) Models based on artificial neural networks (ANN) have shown better accuracy. When considering all indices, the most precision model was the GRNN, which uses as input parameters for modeling the coordinates of sampling points and the distance to the proba.
My Access and Writing Error Corrections of EFL College Pre intermediate Students

This study aims to examine the effects of Automated Writing Evaluation (AWE) feedback on pre intermediate EFL students' writing performance. Furthermore, the purpose of this study is to inspect the effects of AWE on self correction in multiple submissions.  This study employs both qualitative and quantitative methods. The participants were students whose English proficiency was at the pre intermediate level, below CEFR B1. They were enrolled in a two hour, 18 weeks elective college English writing course for non English majors. Twenty seven students completed at least two submissions of drafts on an essay prompt. Through convenience sampling, two male and three female senior students majoring in Business, Chinese and Accounting participated in face to fact interviews.  This study uses descriptive statistics and correlational analysis to evaluate the data. Research data were obtained during 18 week period. My Access was used as an auxiliary writing tool in the college English writing course for non English majors. Students' writing performance, self correction with My Access feedback and self reported perceptions of using My Access were used as the instruments.  The findings of the study can be summarized as follows: 1) based on analyses of qualitative data the individual student made improvement in various writing traits in revisions; 2) while the students were more capable of self correcting in usage type of lexical and syntactic errors using AWE, they were relatively incapable of handling independently mechanics and style types of errors; 3) the results of the interviews and self reported student perceptions of My Access confirmed the effectiveness of AWE feedback in revisions and self correction; 4) although findings of this study supported positive effects of My Access feedback for independent revision and correction, the importance of teachers' role in writing instruction and periodic teacher student interactions in enhancing particular writing skills is stressed.  The application of AWE influences writing instruction in both ESL and EFL contexts by both assessing strengths and weaknesses of student writing and enhancing writing quality. Investigating the effectiveness of AWE feedback in the meditational process of constructing and revising text is thus of great importance to ensure its validity and usefulness. Utilizing AWE can, no doubt, be effective with the participation of teachers. Writing teachers play a crucial role in assisting and guiding students in the writing process.
Effectiveness of dry needling for chronic nonspecific neck pain: a randomized, single blinded, clinical trial
Venue: Pain

Chronic neck pain attributed to a myofascial pain syndrome is characterized by the presence of muscle contractures referred to as myofascial trigger points. In this randomized, parallel group, blinded, controlled clinical trial, we examined the effectiveness of deep dry needling (DDN) of myofascial trigger points in people with chronic nonspecific neck pain. The study was conducted at a public Primary Health Care Centre in Madrid, Spain, from January 2010 to December 2014. A total of 130 participants with nonspecific neck pain presenting with active myofascial trigger points in their cervical muscles were included. These participants were randomly allocated to receive: DDN plus stretching (n 65) or stretching only (control group [n 65] Four sessions of treatment were applied over 2 weeks with a 6 month follow up after treatment. Pain intensity, mechanical hyperalgesia, neck active range of motion, neck muscle strength, and perceived neck disability were measured at baseline, after 2 sessions of intervention, after the intervention period, and 15, 30, 90, and 180 days after the intervention. Significant and clinically relevant differences were found in favour of dry needling in all the outcomes (all P 0.001) at both short and long follow ups. Deep dry needling and passive stretching is more effective than passive stretching alone in people with nonspecific neck pain. The results support the use of DDN in the management of myofascial pain syndrome in people with chronic nonspecific neck pain.
Anonymity Based Privacy Preserving Network Data Publication
Venue: 2016 IEEE Trustcom/BigDataSE/ISPA

Network trace data provide valuable information which contributes to model the network behaviors, defend network attacks and develop new protocols, so releasing the data of network trace is highly demanded by researchers and organizations to promote the development of the network technologies. However, due to the sensitive nature of network trace data, it is a potential risk for organizations to publish the original data which may expose their commercial confidentiality and the customers' privacy within their networks. Several methods to defend the network trace attacks such as statistical fingerprinting and injection have been proposed, unfortunately, they are not enough to protect the privacy because the correspondence between the source and destination IP addresses can also help the adversary to identify the target host. In this paper, we extract the inherent graph structure between the source and destination IP addresses in network trace data, and use k anonymity to prevent the target host from being identified. Combined with other protection techniques, our method can also prevent the fingerprinting and injection attacks.
Graph based privacy preserving data publication
Venue: IEEE INFOCOM 2016 The 35th Annual IEEE International Conference on Computer Communications

We propose a graph based framework for privacy preserving data publication, which is a systematic abstraction of existing anonymity approaches and privacy criteria. Graph is explored for dataset representation, background knowledge specification, anonymity operation design, as well as attack inferring analysis. The framework is designed to accommodate various datasets including social networks, relational tables, temporal and spatial sequences, and even possible unknown data models. The privacy and utility measurements of the anonymity datasets are also quantified in terms of graph features. Our experiments show that the framework is capable of facilitating privacy protection by different anonymity approaches for various datasets with desirable performance.
Structured sparse CCA for brain imaging genetics via graph OSCAR
Venue: BMC Systems Biology

BackgroundRecently, structured sparse canonical correlation analysis (SCCA) has received increased attention in brain imaging genetics studies. It can identify bi multivariate imaging genetic associations as well as select relevant features with desired structure information. These SCCA methods either use the fused lasso regularizer to induce the smoothness between ordered features, or use the signed pairwise difference which is dependent on the estimated sign of sample correlation. Besides, several other structured SCCA models use the group lasso or graph fused lasso to encourage group structure, but they require the structure/group information provided in advance which sometimes is not available.ResultsWe propose a new structured SCCA model, which employs the graph OSCAR (GOSCAR) regularizer to encourage those highly correlated features to have similar or equal canonical weights. Our GOSCAR based SCCA has two advantages: 1) It does not require to pre define the sign of the sample correlation, and thus could reduce the estimation bias. 2) It could pull those highly correlated features together no matter whether they are positively or negatively correlated. We evaluate our method using both synthetic data and real data. Using the 191 ROI measurements of amyloid imaging data, and 58 genetic markers within the APOE gene, our method identifies a strong association between APOE SNP rs429358 and the amyloid burden measure in the frontal region. In addition, the estimated canonical weights present a clear pattern which is preferable for further investigation.ConclusionsOur proposed method shows better or comparable performance on the synthetic data in terms of the estimated correlations and canonical loadings. It has successfully identified an important association between an Alzheimer's disease risk SNP rs429358 and the amyloid burden measure in the frontal region.