nils-herrmann/clean_scirepeval huggingface dataset:
Mining big data using parsimonious factor , machine learning , variable selection and shrinkage methods

A number of recent studies in the economics literature have focused on the usefulness of factor models in the context of prediction using ‘‘big data’’ (see Bai and Ng, 2008; Dufour and Stevanovic, 2010; Forni, Hallin, Lippi, & Reichlin, 2000; Forni et al., 2005; Kim and Swanson, 2014a; Stock and Watson, 2002b, 2006, 2012, and the references cited therein). We add to this literature by analyzing whether ‘‘big data’’ are useful for modelling low frequency macroeconomic variables, such as unemployment, inflation and GDP. In particular, we analyze the predictive benefits associated with the use of principal component analysis (PCA), independent component analysis (ICA), and sparse principal component analysis (SPCA). We also evaluate machine learning, variable selection and shrinkage methods, including bagging, boosting, ridge regression, least angle regression, the elastic net, and the non-negative garotte. Our approach is to carry out a forecasting ‘‘horse-race’’ using prediction models that are constructed based on a variety of model specification approaches, factor estimation methods, and data windowing methods, in the context of predicting 11 macroeconomic variables that are relevant to monetary policy assessment. In many instances, we find that various of our benchmark models, including autoregressive (AR)models, ARmodelswith exogenous variables, and (Bayesian) model averaging, do not dominate specifications based on factor-type dimension reduction combinedwith variousmachine learning, variable selection, and shrinkagemethods (called ‘‘combination’’ models). We find that forecast combination methods are mean square forecast error (MSFE) ‘‘best’’ for only three variables out of 11 for a forecast horizon of h = 1, and for four variables when h = 3 or 12. In addition, non-PCA type factor estimation methods yield MSFE-best predictions for nine variables out of 11 for h = 1, although PCA dominates at longer horizons. Interestingly, we also find evidence of the usefulness of combinationmodels for approximately half of our variableswhen h > 1.Most importantly, we present strong new evidence of the usefulness of factor-based dimension reduction when utilizing ‘‘big data’’ for macroeconometric forecasting.  E-mail addresses: khdouble@bok.or.kr (H.H. Kim), nswanson@econ.rutgers.edu (N.R. Swanson).
On the Confrontation Between Masculinism and Feminism in The Great Gatsby ∗

In The Great Gatsby, Fitzgerald depicts the conflicts and contradictions between men and women about society, family, love, and money, literally mirroring the patriarchal society constantly challenged by feminism in the 1920s of America. This paper intends to compare the features of masculinism and feminism in three aspects: gender, society, and morality. Different identifications of gender role between men and women lead to female protests against male superiority and pursuits of individual liberation. Meanwhile, male unshaken egotism and gradually expanded individualism of women enable them both in lack of sound moral standards. But compared with the female, male moral pride drives them with much more proper moral judge, which reflects Fitzgerald’s support of the masculine society. Probing into the confrontation between masculinism and feminism, it is beneficial for further study on how to achieve equal coexistence and harmony between men and women.
Achieving and Managing Availability SLAs with ITIL Driven Processes, DevOps, and Workflow Tools

System and application availability continues to be a fundamental characteristic of IT services. In recent years the IT Operations team at Wolters Kluwer CT Corporation has placed special focus on this area. Using a combination of goals, metrics, processes, organizational models, communication methods, corrective maintenance, root cause analysis, preventative engineering, automated alerting, and workflow automation significant progress has been made in meeting availability SLAs or Service Level Agreements. This paper presents the background of this work, approach, details of its implementation, and results. A special focus is provided on the use of a classical ITIL view as operationalized in an Agile and DevOps environment.  System Availability, Software Reliability, ITIL, Workflow Automation, Process Engineering, Production Support, Customer Support, Product Support, Change Management, Release Management,  Agile, DevOps, Service Level Agreements, Software Measurement, Microsoft SharePoint.
Was wir über das Lernverhalten unserer Studierenden wissen. Welche Faktoren beeinflussen den Lernerfolg?

Im ZEITLast-Projekt 1 wurde per Zeitbudget-Methode täglich fünf Monate lang in 27 Stichproben aus unterschiedlichen Fächern und Hochschulen die Workload der Bachelor-Studierenden detailliert erhoben. Die minutiöse Erhebung und Analyse der Workload hatte zu der überraschenden Ei nsicht geführt, dass das subjektive Empfinden von Zeit und Belastung in keiner Weise der objektiv gemessenen Zeit entspricht. Die Analyse offenbart nicht nur die Schwäche des unbetreuten Selbststudiums, sondern öffnet den Blick zu gleich für die enorme Diversität in Motivation und Lernverhalten. Während Studierend mit einem zeitlichen Einsatz unter 20 Stunden pro Woche keine/einige/alle Pr üfungen bestanden, bestanden Studierende mit einem zeitlichen Einsatz über 40 Stunden pro Woche etliche Prüfungen nicht und vice versa. Es konnte na chgewiesen werden, dass die Zeit, die Studierende im Studium aufwenden, keine korrelative Beziehung zum Prüfungserfolg eingeht. Die hohe individuelle und interindividuelle Varianz selbst im Präsenzstudium, aber besonders im Selbststudiums führt zur Einsicht, dass es weder die bzw. den Normalstudierenden gibt noch einen normalen gleichförmigen Studienverlauf . Differenziert man jedoch die Studierenden nach ihrer Motivation, so we rden Gruppenprofile von Studierenden erkennbar, die sich doch nach Workload und Studienerfolg unterscheiden. Für die Hochschulen und die Hochschuldidaktik ist es deshalb wichtig, nach Studienstrukturen zu suchen, in denen der He t rogenität Rechnung getragen wird. Um diese Erkenntnisse abzusichern, wurden in einer Metastudie 2 300 empirische Studien aus der Lernforschung zu Workload und Studienerfolg gesichtet. Die Höhe der Workload erweist sich nicht als Prädiktor für den Studienerfolg. Die bek annten demographischen Variablen verlieren im Vergleich zu Variablen des Lernverhaltens wie Gewissenhaftigkeit, Aufmerksamkeit, Konzentration u nd persistente Zielverfolgung an prognostischer Kraft. 1 http://www.zhw.uni-hamburg.de/zhw/?page_id=419 2 Eine ausführliche Darstellung der Metastudie zum Lernv rhalten wird im August 2014 erscheinen: Schulmeister,  Auf der Suche nach Determinanten des St udienerfolgs. In J. Brockmann und A. Pilniok (Hrsg.): Studieneingangsphase in der Rechtswissenschaft. No mos, 2014, S. 72-204.
Programming contextual computations

Modern computer programs are executed in a variety of different contexts: on servers, handheld devices, graphics cards, and across distributed environments, to name a few. Understanding a program’s contextual requirements is therefore vital for its correct execution. This dissertation studies contextual computations, ranging from application-level notions of context to lowerlevel notions of context prevalent in common programming tasks. It makes contributions in three areas: mathematically structuring contextual computations, analysing contextual program properties, and designing languages to facilitate contextual programming. Firstly, existing work which mathematically structures contextual computations using comonads (in programming and semantics) is analysed and extended. Comonads are shown to exhibit a shape preservation property which restricts their applicability to a subset of contextual computations. Subsequently, novel generalisations of comonads are developed, including the notion of an indexed comonad, relaxing shape-preservation restrictions. Secondly, a general class of static analyses called coeffect systems is introduced to describe the propagation of contextual requirements throughout a program. Indexed comonads, with some additional structure, are shown to provide a semantics for languages whose contextual properties are captured by a coeffect analysis. Finally, language constructs are presented to ease the programming of contextual computations. The benefits of these language features, the mathematical structuring, and coeffect systems are demonstrated by a language for container programming which guarantees optimisations and safety invariants.
UNNA: A Unified Neural Network for Aesthetic Assessment

Automatic photo assessment is a high emerging research field with wide useful ‘real-world’ applications. Due to the recent advances in deep learning, one can observe very promising approaches in the last years. However, the proposed solutions are adapted and optimized for ‘isolated’ datasets making it hard to understand the relationship between them and to benefit from the complementary information. Following a unifying approach, we propose in this paper a learning model that integrates the knowledge from different datasets. We conduct a study based on three representative benchmark datasets for photo assessment. Instead of developing for each dataset a specific model, we design and adapt sequentially a unique model which we nominate UNNA. UNNA consists of a deep convolutional neural network, that predicts for a given image three kinds of aesthetic information: technical quality, high-level semantical quality, and a detailed description of photographic rules. Due to the sequential adaptation that exploits the common features between the chosen datasets, UNNA has comparable performances with the state-of-the-art solutions with effectively less parameter. The final architecture of UNNA gives us some interesting indication of the kind of shared features as well as individual aspects of the considered datasets.
FPGA-Based Accelerator for Deep Convolutional Neural Networks for the SPARK Environment

Deep Convolutional Neural Networks are known for their high performance. However, their complexity is one of their most challenging aspects. In this paper, we propose a design of an FPGA-based accelerator for the distributed training of convolutional neural networks. The accelerator is intended for use in the SPARK run time environment to accelerate the training of deep convolutional neural networks in the data center. Our accelerator is very energy efficient and achieves 40 to 250 times speedup in the computation of the multi-layer convolution operation, a key operation in the training/inference of deep networks.
AUTOSAR migration from existing automotive software

The AUTOSAR consortium was founded to manage the growing electronics complexity and improve cost-efficiency without any compromises with quality as well as reusability. It is expected that AUTOSAR, open and standardized automotive software architecture, is widespread in automotive industry worldwide. In general, automotive embedded software has been closely coupled with the hardware, and the boundary between application software and hardware related software is not clear. The AUTOSAR approach requires a more different approaching way to develop automotive embedded software than today. In AUTOSAR, the application software and infrastructural software are clearly separated through the concept of RTE (Run Time Environment), and the paradigm of design is shifted from coding to configuration. By introducing AUTOSAR, existing system need to be translated into AUTOSAR and, several migration concepts are necessary for the successful transition. ECU developments typically start with legacy software, and it is unavoidable to rearrange existing software according to the AUTOSAR concept. This paper shows how to construct AUTOSAR application software components and basic software modules for already developed ECU software.
Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion

This research analyses the suburban expansion in the metropolitan area of Tehran, Iran. A hybrid model consisting of logistic regression model, Markov chain (MC), and cellular automata (CA) was designed to improve the performance of the standard logistic regression model. Environmental and socio-economic variables dealing with urban sprawl were operationalised to create a probability surface of spatiotemporal states of built-up land use for the years 2006, 2016, and 2026. For validation, the model was evaluated by means of relative operating characteristic values for different sets of variables. The approach was arkov chain ellular automata ehran calibrated for 2006 by cross comparing of actual and simulated land use maps. The achieved outcomes represent a match of 89% between simulated and actual maps of 2006, which was satisfactory to approve the calibration process. Thereafter, the calibrated hybrid approach was implemented for forthcoming years. Finally, future land use maps for 2016 and 2026 were predicted by means of this hybrid approach. The simulated maps illustrate a new wave of suburban development in the vicinity of Tehran at the tropo western border of the me
A Health-IoT Platform Based on the Integration of Intelligent Packaging, Unobtrusive Bio-Sensor, and Intelligent Medicine Box

In-home healthcare services based on the Internet-of-Things (IoT) have great business potential; however, a comprehensive platform is still missing. In this paper, an intelligent home-based platform, the iHome Health-IoT, is proposed and implemented. In particular, the platform involves an open-platform-based intelligent medicine box (iMedBox) with enhanced connectivity and interchangeability for the integration of devices and services; intelligent pharmaceutical packaging (iMedPack) with communication capability enabled by passive radio-frequency identification (RFID) and actuation capability enabled by functional materials; and a flexible and wearable bio-medical sensor device (Bio-Patch) enabled by the state-of-the-art inkjet printing technology and system-on-chip. The proposed platform seamlessly fuses IoT devices (e.g., wearable sensors and intelligent medicine packages) with in-home healthcare services (e.g., telemedicine) for an improved user experience and service efficiency. The feasibility of the implemented iHome Health-IoT platform has been proven in field trials.
Methods of the Water-Energy-Food Nexus

This paper focuses on a collection of methods that can be used to analyze the water-energy-food (WEF) nexus. We classify these methods as qualitative or quantitative for interdisciplinary and transdisciplinary research approaches. The methods for interdisciplinary research approaches can be used to unify a collection of related variables, visualize the research problem, evaluate the issue, and simulate the system of interest. Qualitative methods are generally used to describe the nexus in the region of interest, and include primary research methods such as Questionnaire Surveys, as well as secondary research methods such as Ontology Engineering and Integrated Maps. Quantitative methods for examining the nexus include Physical Models, Benefit-Cost Analysis (BCA),
Twenty-eight years of component-based software engineering

The idea of developing software components was envisioned more than forty years ago. In the past two decades, Component-Based Software Engineering (CBSE) has emerged as a distinguishable approach in software engineering, and it has attracted the attention of many researchers, which has led to many results being published in the research literature. There is a huge amount of knowledge encapsulated in conferences and journals targeting this area, but a systematic analysis of that knowledge is missing. For this reason, we aim to investigate the state-of-the-art of the CBSE area through a detailed literature review. To do this, 1231 studies dating from 1984 to 2012 were analyzed. Using the available evidence, this paper addresses five dimensions of CBSE: main objectives, research topics, application domains, research intensity and applied research methods. The main objectives found were to increase productivity, save costs and improve quality. The most addressed application domains are homogeneously divided between commercial-off-the-shelf (COTS), distributed and embedded systems. Intensity of research showed a considerable increase in the last fourteen years. In addition to the analysis, this paper also synthesizes the available evidence, identifies open issues and points out areas that call for further research.
Why do some resource-abundant countries succeed while others do not ?

On average, resource-abundant countries have experienced lower growth over the last four decades than their resource-poor counterparts. But the most interesting aspect of the paradox of plenty is not the average effect of natural resources, but its variation. For every Nigeria or Venezuela there is a Norway or a Botswana. Why do natural resources induce prosperity in some countries but stagnation in others? This paper gives an overview of the dimensions along which resource-abundant winners and losers differ. In light of this, it then discusses different theory models of the resource curse, with a particular emphasis on recent developments in political economy.
Circulating tumor cells: liquid biopsy of cancer.

The detection and molecular characterization of circulating tumor cells (CTCs) are one of the most active areas of translational cancer research, with >400 clinical studies having included CTCs as a biomarker. The aims of research on CTCs include (a) estimation of the risk for metastatic relapse or metastatic progression (prognostic information), (b) stratification and real-time monitoring of therapies, (c) identification of therapeutic targets and resistance mechanisms, and (d) understanding metastasis development in cancer patients.



This review focuses on the technologies used for the enrichment and detection of CTCs. We outline and discuss the current technologies that are based on exploiting the physical and biological properties of CTCs. A number of innovative technologies to improve methods for CTC detection have recently been developed, including CTC microchips, filtration devices, quantitative reverse-transcription PCR assays, and automated microscopy systems. Molecular-characterization studies have indicated, however, that CTCs are very heterogeneous, a finding that underscores the need for multiplex approaches to capture all of the relevant CTC subsets. We therefore emphasize the current challenges of increasing the yield and detection of CTCs that have undergone an epithelial-mesenchymal transition. Increasing assay analytical sensitivity may lead, however, to a decrease in analytical specificity (e.g., through the detection of circulating normal epithelial cells).



A considerable number of promising CTC-detection techniques have been developed in recent years. The analytical specificity and clinical utility of these methods must be demonstrated in large prospective multicenter studies to reach the high level of evidence required for their introduction into clinical practice.
Developing standards for reporting implementation studies of complex interventions (StaRI): a systematic review and e-Delphi

Dissemination and implementation of health care interventions are currently hampered by the variable quality of reporting of implementation research. Reporting of other study types has been improved by the introduction of reporting standards (e.g. CONSORT). We are therefore developing guidelines for reporting implementation studies (StaRI).



Using established methodology for developing health research reporting guidelines, we systematically reviewed the literature to generate items for a checklist of reporting standards. We then recruited an international, multidisciplinary panel for an e-Delphi consensus-building exercise which comprised an initial open round to revise/suggest a list of potential items for scoring in the subsequent two scoring rounds (scale 1 to 9). Consensus was defined a priori as 80% agreement with the priority scores of 7, 8, or 9.



We identified eight papers from the literature review from which we derived 36 potential items. We recruited 23 experts to the e-Delphi panel. Open round comments resulted in revisions, and 47 items went forward to the scoring rounds. Thirty-five items achieved consensus: 19 achieved 100% agreement. Prioritised items addressed the need to: provide an evidence-based justification for implementation; describe the setting, professional/service requirements, eligible population and intervention in detail; measure process and clinical outcomes at population level (using routine data); report impact on health care resources; describe local adaptations to the implementation strategy and describe barriers/facilitators. Over-arching themes from the free-text comments included balancing the need for detailed descriptions of interventions with publishing constraints, addressing the dual aims of reporting on the process of implementation and effectiveness of the intervention and monitoring fidelity to an intervention whilst encouraging adaptation to suit diverse local contexts.



We have identified priority items for reporting implementation studies and key issues for further discussion. An international, multidisciplinary workshop, where participants will debate the issues raised, clarify specific items and develop StaRI standards that fit within the suite of EQUATOR reporting guidelines, is planned.
From STEM to STEAM : the Neuroscience Behind the Movement Towards Arts Integration in K-12 Curricula

As a result of disparities in the educational system, numerous scholars and educators across disciplines currently support the STEAM (Science, Technology, Engineering, Art, and Mathematics) movement for arts integration. An educational approach to learning focusing on guiding student inquiry, dialogue, and critical thinking through interdisciplinary instruction, STEAM values proficiency, knowledge, and understanding. Despite extant literature urging for this integration, the trend has yet to significantly influence federal or state standards for K-12 education in the United States. This paper provides a brief and focused review of key theories and research from the fields of cognitive psychology and neuroscience outlining the benefits of arts integrative curricula in the classroom. Cognitive psychologists have found that the arts improve participant retention and recall through semantic elaboration, generation of information, enactment, oral production, effort after meaning, emotional arousal, and pictorial representation. Additionally, creativity is considered a higher-order cognitive skill and EEG results show novel brain patterns associated with creative thinking. Furthermore, cognitive neuroscientists have found that long-term artistic training can augment these patterns as well as lead to greater plasticity and neurogenesis in associated brain regions. Research suggests that artistic training increases retention and recall, generates new patterns of thinking, induces plasticity, and results in strengthened higher-order cognitive functions related to creativity. These benefits of arts integration, particularly as approached in the STEAM movement, are what develops students into adaptive experts that have the skills to then contribute to innovation in a variety of disciplines.