nils-herrmann/clean_scirepeval huggingface dataset:
Refocusing distance of a standard plenoptic camera.
Venue: Optics express

Recent developments in computational photography enabled variation of the optical focus of a plenoptic camera after image exposure, also known as refocusing. Existing ray models in the field simplify the camera's complexity for the purpose of image and depth map enhancement, but fail to satisfyingly predict the distance to which a photograph is refocused. By treating a pair of light rays as a system of linear functions, it will be shown in this paper that its solution yields an intersection indicating the distance to a refocused object plane. Experimental work is conducted with different lenses and focus settings while comparing distance estimates with a stack of refocused photographs for which a blur metric has been devised. Quantitative assessments over a 24 m distance range suggest that predictions deviate by less than 0.35 in comparison to an optical design software. The proposed refocusing estimator assists in predicting object distances just as in the prototyping stage of plenoptic cameras and will be an essential feature in applications demanding high precision in synthetic focus or where depth map recovery is done by analyzing a stack of refocused photographs.
Unlock the Stock: User Topic Modeling for Stock Market Analysis
Venue: EDBT/ICDT Workshops

The increasing use of Twitter as a medium for sharing news related to various topics, facilitates methods for automatic news creation or event detection and prediction. However, these methods are hindered by users posting and propagating incorrect or irrelevant content. Choosing the right users is crucial in order to sample down the tweets to be analyzed, and preserve the quality of the predicted events or generated news. In this paper, we present an e ective method for identifying expert users in defined areas related to the stock market. For each user we generate a model based on the content of their posts. The model represents the domains the user talks about, and allows a selection of users for various tasks. We show the e ectiveness of the proposed approach by performing a series of experiments using large Twitter datasets related to Stock Market Companies.
Exploiting abused trending topics to identify spam campaigns in Twitter
Venue: Social Network Analysis and Mining

Twitter is an online social network (OSN) with approximately 650 million users. It has been fairly characterized as one of the most influential OSNs since it includes public figures, organizations, news media and official authorities. Twitter has an inherent simple philosophy with short messages, friendship relations, hashtags and support for media sharing such as photos and short videos. Popular hashtags that emerge from users' activity are displayed prominently in the platform as Popular Trends. Unfortunately, the capabilities of the platform can be also abused and exploited for distributing illicit content or boosting false information, and the consequences of such actions can be really severe: one false tweet was enough for making the stock market crash for a short period of time in 2013. In this study, we make an experimental analysis on a large dataset containing 150 million tweets. We delve into the dynamics of the popular trends as well as other Twitter features in regard to deliberate misuse. We investigate traditional spam techniques as well as an obfuscated way of spam campaigns that exploit trending topics and hides malicious URLs within Google search result links. We implement a simple and lightweight classifier for indentifying spam users as well as spam tweets. Finally, we visualize these spam campaigns and investigate their inner properties.
Coalition Formation for Cooperative Service Based Message Sharing in Vehicular Ad Hoc Networks
Venue: IEEE Transactions on Parallel and Distributed Systems

Reliable message delivery is a challenging task in Vehicular Ad Hoc Networks (VANETs) Current literature on VANETs generalize all messages and use the same strategy to transmit them. In this paper, we model the cooperative service based message sharing problem in VANETs as a coalition formation game among nodes. Nodes associate with a coalition based on the type of service message they process. In the proposed model, service messages are distinguished from one another by their types. Nodes process different types of service messages and form a coalition based on the type of messages they process at that time. Some nodes within a coalition can work as a relay, which is modeled as a network formation game to select exactly one relay among a group of potential relay nodes to improve efficiency of the network in terms of improved packet reception rate and reducing transmission delay. The nodes form independent disjoint coalitions and tree structure is formed with the relay nodes within a coalition by using the proposed algorithm, COMES. Simulation results show that COMES, which allows nodes to form independent coalitions among themselves, improves the network performance in terms of incentive received by players by at least 40 percent compared to a non cooperative function.
The Influence of Leadership in Building a Learning Organization

Educational institutions as learning organizations could be best explained through Senge's theoretical framework of schools as learning organizations. For Peter Senge, 'real learning gets to the heart of what it is to be human. We become able to recreate ourselves. This applies to both individuals and organizations. Thus, for a learning organization, it is not enough to survive, but to enhance capacity to create' Educational leaders are individuals in schools, colleges, universities and educational institutions that have responsibilities and a decision making role. These people are responsible for the successes and failures of their institutions. This paper discusses the potential of leadership styles in influencing a learning organization. It first explores the leadership styles most frequently engaged in schools and secondly identifies the dimensions of leadership style that influence a learning organization. The study was conducted across 80 schools in the city of Bangalore. Two tools were used in this study, namely, Learning Organization Profile (LOP) to measure learning organization and leadership styles scale to measure the leadership style most engaged in by the supervisors/principals/coordinators of schools. A total of 750 respondents' data was analyzed. The study has indicated that leadership styles play a significant role in building a learning organization. The paper concludes with a discussion of the implications.
IMPROVING TRUST THROUGH ETHICAL LEADERSHIP: MOVING BEYOND THE SOCIAL LEARNING THEORY TO A HISTORICAL LEARNING APPROACH

The complex nature of trust and its evolving relative concepts require a more idealistic and simpler review. Ethical leadership is related to trust, honesty, transparency, compassion, empathy, results orientedness, and many other behavioral attributes. Ethical leadership and good leadership are the same, because they represent practicing what one preaches or showing a way to the accomplishment of set goals. The outcomes and findings of many research papers on trust and ethical leadership report positive correlations between ethical leadership and trust. Improving trust from different rational standpoints requires moving and looking beyond the popular theoretical framework through which most results are derived in order to create a new thinking perspective. Social learning theory strongly emphasizes modelling while the new historical learning approach, proposed by the author, is defined as an approach that creates unique historical awareness among individuals, groups, institutions, societies, and nations to use previous experience(s) or occurrence(s) as a guide in developing positive opinion(s) and framework(s) in order to tackle the problems and issues of today and tomorrow. Social learning theory is seen as limited from the perspectives of balancing the equation between leadership and trust, the non compatibility of the values of different generations at work, and other approaches and methods that support the historical approach.  The main contention is that a historical learning approach can inform an independent learning to improve trust and its relatives (e.g. motivation and performance) because independent learning can positively shape the value of integrity, which is an integral part of ethical leadership. Historical learning can positively shape leadership in every perspective, because good leadership can develop based on history and past experience; this in return helps in improving trust at every level.
Factor structure and validity of the State Trait Inventory for Cognitive and Somatic Anxiety.
Venue: Psychological assessment

The State Trait Inventory for Cognitive and Somatic Anxiety (STICSA; Ree, French, MacLeod, Locke, 2008) is a relatively new measure of anxiety. The current research investigated the factor structure and reliability of scores on the STICSA and the validity of the interpretation of STICSA scores in a sample of undergraduate students. Participants completed a battery of self report questionnaires online, including measures of anxiety, depression, affect, and social desirability. Scores on the 4 subscales of the STICSA Trait Cognitive, Trait Somatic, State Cognitive, and State Somatic exhibited good internal consistencies (as .92) Results of a confirmatory factor analysis provided support for a hierarchical model of the STICSA including a global anxiety factor plus 4 specific factors corresponding to the STICSA subscales. Support was also found for a four factor model, with factors corresponding to the STICSA subscales. Pearson product moment correlations with other measures of anxiety provided evidence of the convergent validity of the interpretation of STICSA scores, and Pearson product moment correlations with measures of depression and affect provided evidence of the divergent validity of the interpretation of STICSA scores. The STICSA is the only existing self report anxiety measure that contains scales measuring state and trait anxiety as well as cognitive and somatic anxiety. Comparisons between the convergent and divergent validity of test score interpretations of the STICSA and the State Trait Anxiety Inventory (STAI; Spielberger et al. 1983) revealed that the STICSA has better convergent validity with measures of somatic anxiety and better divergent validity with measures of depression and affect.
Harold Jeffreys's default Bayes factor hypothesis tests: Explanation, extension, and application in psychology

pioneered the development of default Bayes factor hypothesis tests for standard statistical problems. Using Jeffreys's Bayes factor hypothesis tests, researchers can grade the decisiveness of the evidence that the data provide for a point null hypothesis H 0 versus a composite alternative hypothesis H 1 Consequently, Jeffreys's tests are of considerable theoretical and practical relevance for empirical researchers in general and for experimental psychologists in particular. To highlight this relevance and to facilitate the interpretation and use of Jeffreys's Bayes factor tests we focus on two common inferential scenarios: testing the nullity of a normal mean (i.e. the Bayesian equivalent of the t test) and testing the nullity of a correlation. For both Bayes factor tests, we explain their development, we extend them to one sided problems, and we apply them to concrete examples from experimental psychology.
Data Narratives: Uncovering tensions in personal data management
Venue: CSCW

We present an interview study of 34 participants in the US and Korea who described how they manage their personal data, from work files to family photos. Through their 'data narratives' accounts of their data management practices, including device usage patterns and negotiations with system and brand ecosystems' we explore how individuals negotiate a complex, multi service, and morally charged sociotechnical landscape, balancing demands to share and to safeguard their data in appropriate ways against a shifting background of changing technologies, relationships, individuals, and corporations. We describe the guiding framework that people use to make decisions as a 'moral economy' of data management, contributing to our understanding of context specific system choices.
Opportunities and challenges in the use of personal health data for health research
Venue: J. Am. Medical Informatics Assoc.

Understand barriers to the use of personal health data (PHD) in research from the perspective of three stakeholder groups: early adopter individuals who track data about their health, researchers who may use PHD as part of their research, and companies that market self tracking devices, apps or services, and aggregate and manage the data that are generated.  A targeted convenience sample of 465 individuals and 134 researchers completed an extensive online survey. Thirty five hour long semi structured qualitative interviews were conducted with a subset of 11 individuals and 9 researchers, as well as 15 company/key informants.  Challenges to the use of PHD for research were identified in six areas: data ownership; data access for research; privacy; informed consent and ethics; research methods and data quality; and the unpredictable nature of the rapidly evolving ecosystem of devices, apps, and other services that leave "digital footprints." Individuals reported willingness to anonymously share PHD if it would be used to advance research for the good of the public. Researchers were enthusiastic about using PHD for research, but noted barriers related to intellectual property, licensing, and the need for legal agreements with companies. Companies were interested in research but stressed that their first priority was maintaining customer relationships.  Although challenges exist in leveraging PHD for research, there are many opportunities for stakeholder engagement, and experimentation with these data is already taking place. These early examples foreshadow a much larger set of activities with the potential to positively transform how health research is conducted.
Improved Prediction of the Pathologic Stage of Patient With Prostate Cancer Using the CART PSO Optimization Analysis in the Korean Population
Venue: Technology in Cancer Research Treatment

In current practice, medical experts use the pathological stage predictions provided in the Partin tables to support their decisions. Hence, the Partin tables are based on logistic regression built from the US data. In the present study, we developed a data mining model to predict the pathologic stage of prostate cancer. In this newly developed model, using the classification and regression tree particle swarm optimization analysis of the Korean population data, we aim to improve the prediction accuracy of the pathologic state of prostate cancer.  A total of 467 patients from the smart prostate cancer database were evaluated. The results were intended to predict the pathologic stage of prostate cancer: organ confined disease and non organ confined disease. The accuracy of 4 classification and regression tree particle swarm optimization models was compared; furthermore, the models were validated with the Partin tables using the receiver operating characteristic curve.  Among the 467 evaluated patients, 235 patients had organ confined disease and 232 patients had non organ confined disease. The area under the receiver operating characteristic curve of the proposed classification and regression tree particle swarm optimization model (0.858 0.034) was larger than the 1 in the Partin tables (0.666 0.046)  The proposed classification and regression tree particle swarm optimization model was superior to the Partin tables in terms of predicting the risk of prostate cancer. Compared to the validation of the Partin tables for the Korean population, the classification and regression tree particle swarm optimization model resulted in a larger receiver operating characteristic curve and a more accurate prediction of the pathologic stage of prostate cancer in the Korean population.
Active phase, catalytic activity, and induction period of Fe/zeolite material in nonoxidative aromatization of methane

Catalytic dehydroaromatization of methane has been investigated over 2 6 wt. Fe modified HZSM 5 or HMCM 22 zeolite in the absence of an added oxidant. After pretreatment in a CH 4 /Ar/He mixture, Fe/HMCM 22, and more significantly, Fe/HZSM 5 exhibited high catalytic activity in nonoxidative conversion of methane to aromatic hydrocarbons. This conversion actually takes part in three consecutive steps, oxidation of methane by iron oxides, decomposition, and aromatization of methane. During this conversion, XPS and XRD results showed the gradual reduction of Fe 2 O 3 to metallic Fe, followed by the carburization of the metallic species. The fact that no aromatic products were detected until iron carbide was formed demonstrated that carburized Fe was the active species for the aromatization of methane. Under the same reaction conditions, both metallic Fe and carburized Fe were highly effective for activation of methane, but they catalyzed dehydrogenation of methane to carbonaceous materials and to aromatics, respectively. Difference in the catalytic properties of those two Fe species suggested that carburized Fe could stabilize carbene, the primary intermediate of activated methane, possibly via formation of a metal carbene complex, C Fe CH 2 so that carbene was able to dimerize into ethylene, which was then oligomerized to aromatics on the acid sites of the zeolite.
Carnosic acid biosynthesis elucidated by a synthetic biology platform
Venue: Proceedings of the National Academy of Sciences

Here we report on the development of a modular terpene production platform as a screening tool for the functional characterization of next generation sequencing information and on the application of this system in the characterization of the biosynthetic pathway leading to the potent antioxidant carnosic acid in Lamiaceae. In addition to revealing an important plant natural product biosynthetic pathway, this work highlights the usefulness of synthetic biology approaches in the elucidation of plant natural product biosynthetic pathways and develops yeast as a tool to exploit the wealth of next generation sequencing data that are constantly being produced through transcriptomic, genomic, or metagenomic studies. The approach described here will be useful to numerous researchers studying terpene biosynthesis in plants and microorganisms. Synthetic biology approaches achieving the reconstruction of specific plant natural product biosynthetic pathways in dedicated microbial "chassis" have provided access to important industrial compounds (e.g. artemisinin, resveratrol, vanillin) However, the potential of such production systems to facilitate elucidation of plant biosynthetic pathways has been underexplored. Here we report on the application of a modular terpene production platform in the characterization of the biosynthetic pathway leading to the potent antioxidant carnosic acid and related diterpenes in Salvia pomifera and Rosmarinus officinalis. Four cytochrome P450 enzymes are identified (CYP76AH24, CYP71BE52, CYP76AK6, and CYP76AK8) the combined activities of which account for all of the oxidation events leading to the biosynthesis of the major diterpenes produced in these plants. This approach develops yeast as an efficient tool to harness the biotechnological potential of the numerous sequencing datasets that are increasingly becoming available through transcriptomic or genomic studies.
Characterizing Sierra Nevada Snowpack Using Variable Resolution CESM

AbstractThe location, timing, and intermittency of precipitation in California make the state integrally reliant on winter season snowpack accumulation to maintain its economic and agricultural livelihood. Of particular concern is that winter season snowpack has shown a net decline across the western United States over the past 50 years, resulting in major uncertainty in water resource management heading into the next century. Cutting edge tools are available to help navigate and preemptively plan for these uncertainties. This paper uses a next generation modeling technique variable resolution global climate modeling within the Community Earth System Model (VR CESM) at horizontal resolutions of 0.125deg (14 km) and 0.25deg (28 km) VR CESM provides the means to include dynamically large scale atmosphere ocean drivers, to limit model bias, and to provide more accurate representations of regional topography while doing so in a more computationally efficient manner than can be achieved with conventional general
An evaluation of the variable resolution CESM for modeling California's climate

Author(s) Huang, X; Rhoades, AM; Ullrich, PA; Zarzycki, CM Abstract: (c) 2016. The Authors. In this paper, the recently developed variable resolution option within the Community Earth System Model (VR CESM) is assessed for long term regional climate modeling of California at 0.25deg(28 km) and 0.125deg(14 km) horizontal resolutions. The mean climatology of near surface temperature and precipitation is analyzed and contrasted with reanalysis, gridded observational data sets, and a traditional regional climate model (RCM) the Weather Research and Forecasting (WRF) model. Statistical metrics for model evaluation and tests for differential significance have been extensively applied. With only prescribed sea surface temperatures, VR CESM tended to produce a warmer summer (by about 1 3degC) and overestimated overall winter precipitation (about 25% 35% compared to reference data sets. Increasing resolution from 0.25degto 0.125degdid not produce a statistically significant improvement in the model results. By comparison, the analogous WRF climatology (constrained laterally and at the sea surface by ERA Interim reanalysis) was 1 3degC colder than the reference data sets, underestimated precipitation by 20% 30% at 27 km resolution, and overestimated precipitation by 65 85% at 9 km. Overall, VR CESM produced comparable statistical biases to WRF in key climatological quantities. This assessment highlights the value of variable resolution global climate models (VRGCMs) in capturing fine scale atmospheric processes, projecting future regional climate, and addressing the computational expense of uniform resolution global climate models.