nils-herrmann/clean_scirepeval huggingface dataset:
MOJ Civil Engineering Big Data in Building Information Modeling Research : Survey and Exploratory Text Mining

Building Information Modeling (BIM) has rapidly grown from merely being a three-dimensional (3D) model of a facility to serving as “a shared knowledge resource for information about a facility, forming a reliable basis for decisions during its life cycle from inception onward” . BIM with three primary spatial dimensions (width, height, and depth) becomes 4D BIM when time (construction scheduling information) is added, and 5D BIM when cost information is added to it. Although the sixth dimension of the 6D BIM is often attributed to asset information useful for Facility Management (FM) processes, there is no agreement in the research literature on what each dimension represents beyond the fifth dimension . BIM ultimately seeks to digitize the different stages of a building lifecycle such as planning, design, construction, and operation such that consistent digital information of a building project can be used by stakeholders throughout the building life-cycle . The United States National Building Information Model Standard (NBIMS) initially characterized BIMs as digital representations of physical and functional aspects of a facility. But, in the most recent version released in July 2015, the NBIMS’ definition of BIM includes three separate but linked functions, namely business process, digital representation, and organization and control [4]. A number of national-level initiatives are underway in various countries to formally encourage the adoption of BIM technologies in the Architecture, Engineering, and Construction (AEC) and FM industries. Building SMART, with 18 chapters across the globe, including USA, UK, Australasia, etc., was established in 1995 with the aim of developing and driving the active use of open internationally-recognized standards to support the wider adoption of BIM across the building and infrastructure sectors . The UK BIM Task Group, with experts from industry, government, public sector, institutes, and academia, is committed to facilitate the implementation of ‘collaborative 3D BIM’, a UK Government Construction Strategy initiative . Similarly, the EUBIM Task Group was started with a vision to foster the common use of BIM in public works and produce a handbook containing the common BIM principles, guidance and practices for public contracting entities and policy makers [7].
Desenvolvimento de uma rede neuro-fuzzy para predição da temperatura retal de frangos de corte

The goal of this work was to develop and validate a neuro-fuzzy intelligent system (LOLIMOT) for rectal temperature prediction of broiler chickens. The neuro-fuzzy network was developed using SCILAB 4.1, on the ground of three Departamento de Engenharia, Universidade Federal de Lavras (UFLA), Caixa Postal 3037, Lavras/MG, Brasil le.ferreira@gmail.com yanagi@deg.ufla.br alisonzille@gmail.com Desenvolvimento de uma rede neuro-fuzzy para predição da temperatura retal de frangos de corte 222 RITA • Volume 17 • Número 2 • 2010 input variables: air temperature, relative humidity and air velocity. The output variable was rectal temperature. Experimental results, used for validation, showed that the average standard deviation between simulated and measured values of RT was 0.11 °C. The neuro-fuzzy system presents as a satisfactory hybrid intelligent system for rectal temperature prediction of broiler chickens, which adds fuzzy logic features based on the fuzzy sets theory to artificial neural networks.
Manufacturing Strategy , Capabilities and Performance

This dissertation addresses the topic of manufacturing strategy, especially the manufacturing capabilities and operational performance of manufacturing plants. Manufacturing strategy research aims at providing a structured decision making approach to improve the economics of manufacturing and to make companies more competitive. The overall objective of this thesis is to investigate how manufacturing companies make use of different manufacturing practices or bundles of manufacturing practices to develop certain sets of capabilities, with the ultimate goal of supporting the market requirements. The thesis aims to increase the understanding of the role of operations management and its immediate impact on manufacturing performance. Following the overall research objective three areas are identified to be of particular interest; to investigate (i) the relationship among different dimensions of operational performance, (ii) the way different performance dimensions are affected by manufacturing practices or bundles of manufacturing practices, (iii) whether there are contingencies that may help explain the relationships between dimensions of manufacturing capabilities or the effects of manufacturing practices or bundles of manufacturing practices on operational performance. The empirical elements in this thesis use data from the High Performance Manufacturing (HPM) project. The HPM project is an international study of manufacturing plants involving seven countries and three industries. The research contributes to several insights to the research area of manufacturing strategy and to practitioners in manufacturing operations. The thesis develops measurements for and tests the effects of several manufacturing practices on operational performance. The results are aimed at providing guidance for decision making in manufacturing companies. The most prominent implication for researchers is the manifestation of the customer order decoupling point as an important contingency variable to consider when studying manufacturing operations.
Accurate and Efficient Traffic Sign Detection Using Discriminative AdaBoost and Support Vector Regression

Real-time traffic sign detection and recognition has been receiving increasingly more attention in recent years due to the popularity of driver-assistance systems and autonomous vehicles. This paper proposes an accurate and efficient traffic sign detection technique by exploring AdaBoost and support vector regression (SVR) for discriminative detector learning. Different from the reported traffic sign detection techniques, a novel saliency estimation approach is first proposed, where a new saliency model is built based on the traffic sign-specific color, shape, and spatial information. By incorporating the saliency information, enhanced feature pyramids are built to learn an AdaBoost model that detects a set of traffic sign candidates from images. A novel iterative codeword selection algorithm is then designed to generate a discriminative codebook for the representation of sign candidates, as detected by the AdaBoost, and an SVR model is learned to identify the real traffic signs from the detected sign candidates. Experiments on three public data sets show that the proposed traffic sign detection technique is robust and obtains superior accuracy and efficiency.
How to Do Agent-Based Simulations in the Future : From Modeling Social Mechanisms to Emergent Phenomena and Interactive Systems Design

Since the advent of computers, the natural and engineering sciences have enormously progressed. Computer simulations allow one to understand interactions of physical particles and make sense of astronomical observations, to describe many chemical properties ab initio, and to design energy-efficient aircrafts and safer cars. Today, the use of computational devices is pervasive. Offices, administrations, financial trading, economic exchange, the control of infrastructure networks, and a large share of our communication would not be conceivable without the use of computers anymore. Hence, it would be very surprising, if computers could not make a contribution to a better understanding of social and economic systems. While relevant also for the statistical analysis of data and data-driven efforts to reveal patterns of human interaction , we will focus here on the prospects of computer simulation of social and economic systems. More specifically, we will discuss the techniques of agent-based modeling (ABM) and multi-agent simulation (MAS), including the challenges, perspectives and limitations of the approach. In doing so, we will discuss a number of issues, which have not been covered by the excellent books and review papers available so far [2–10]. In particular, we will describe the different steps belonging to a thorough agent-based simulation study, and try to explain, how to do them right from a scientific perspective. To some extent, computer simulation can be seen as experimental technique for hypothesis testing and scenario analysis, which can be used complementary and in combination with experiments in real-life, the lab or the Web.
Real-Time Bidding Benchmarking with iPinYou Dataset

Being an emerging paradigm for display advertising, RealTime Bidding (RTB) drives the focus of the bidding strategy from context to users’ interest by computing a bid for each impression in real time. The data mining work and particularly the bidding strategy development becomes crucial in this performance-driven business. However, researchers in computational advertising area have been suffering from lack of publicly available benchmark datasets, which are essential to compare different algorithms and systems. Fortunately, a leading Chinese advertising technology company iPinYou decided to release the dataset used in its global RTB algorithm competition in 2013. The dataset includes logs of ad auctions, bids, impressions, clicks, and final conversions. These logs reflect the market environment as well as form a complete path of users’ responses from advertisers’ perspective. This dataset directly supports the experiments of some important research problems such as bid optimisation and CTR estimation. To the best of our knowledge, this is the first publicly available dataset on RTB display advertising. Thus, they are valuable for reproducible research and understanding the whole RTB ecosystem. In this paper, we first provide the detailed statistical analysis of this dataset. Then we introduce the research problem of bid optimisation in RTB and the simple yet comprehensive evaluation protocol. Besides, a series of benchmark experiments are also conducted, including both click-through rate (CTR) estimation and bid optimisation.
Assessing risk for sexual recidivism: some proposals on the nature of psychologically meaningful risk factors.

Risk assessment and treatment for sexual offenders should focus on individual characteristics associated with recidivism risk. Although it is possible to conduct risk assessments based purely on empirical correlates, the most useful evaluations also explain the source of the risk. In this review, the authors propose that the basic requirements for a psychologically meaningful risk factor are (a) a plausible rationale that the factor is a cause of sexual offending and (b) strong evidence that it predicts sexual recidivism. Based on the second of these criteria, the authors categorize potential risk factors according to the strength of the evidence for their relationship with offending. The most strongly supported variables should be emphasized in both assessment and treatment of sexual offenders. Further research is required, however, to establish causal connections between these variables and recidivism and to examine the extent to which changes in these factors leads to reductions in recidivism potential.
What Value Do Explicit High Level Concepts Have in Vision to Language Problems?

Much recent progress in Vision-to-Language (V2L) problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This approach does not explicitly represent high-level semantic concepts, but rather seeks to progress directly from image features to text. In this paper we investigate whether this direct approach succeeds due to, or despite, the fact that it avoids the explicit representation of high-level information. We propose a method of incorporating high-level concepts into the successful CNN-RNN approach, and show that it achieves a significant improvement on the state-of-the-art in both image captioning and visual question answering. We also show that the same mechanism can be used to introduce external semantic information and that doing so further improves performance. We achieve the best reported results on both image captioning and VQA on several benchmark datasets, and provide an analysis of the value of explicit high-level concepts in V2L problems.
Paternal Perinatal Depression Assessed by the Edinburgh Postnatal
Depression Scale and the Gotland Male Depression Scale: Prevalence and Possible
Risk Factors

Several studies have used the Edinburgh Postnatal Depression Scale (EPDS), developed to screen new mothers, also for new fathers. This study aimed to further contribute to this knowledge by comparing assessment of possible depression in fathers and associated demographic factors by the EPDS and the Gotland Male Depression Scale (GMDS), developed for "male" depression screening. The study compared EPDS score ≥10 and ≥12, corresponding to minor and major depression, respectively, in relation to GMDS score ≥13. At 3-6 months after child birth, a questionnaire was sent to 8,011 fathers of whom 3,656 (46%) responded. The detection of possibly depressed fathers by EPDS was 8.1% at score ≥12, comparable to the 8.6% detected by the GMDS. At score ≥10, the proportion detected by EPDS increased to 13.3%. Associations with possible risk factors were analyzed for fathers detected by one or both scales. A low income was associated with depression in all groups. Fathers detected by EPDS alone were at higher risk if they had three or more children, or lower education. Fathers detected by EPDS alone at score ≥10, or by both scales at EPDS score ≥12, more often were born in a foreign country. Seemingly, the EPDS and the GMDS are associated with different demographic risk factors. The EPDS score appears critical since 5% of possibly depressed fathers are excluded at EPDS cutoff 12. These results suggest that neither scale alone is sufficient for depression screening in new fathers, and that the decision of EPDS cutoff is crucial.
Phenotypic heterogeneity of the endothelium: I. Structure, function, and mechanisms.

Endothelial cells, which form the inner cellular lining of blood vessels and lymphatics, display remarkable heterogeneity in structure and function. This is the first of a 2-part review focused on phenotypic heterogeneity of blood vessel endothelium. This review provides an historical perspective of our understanding of endothelial heterogeneity, discusses the scope of phenotypic diversity across the vascular tree, and addresses proximate and evolutionary mechanisms of endothelial cell heterogeneity. The overall goal is to underscore the importance of phenotypic heterogeneity as a core property of the endothelium.
Towards Robust Linguistic Analysis using OntoNotes

Large-scale linguistically annotated corpora have played a crucial role in advancing the state of the art of key natural language technologies such as syntactic, semantic and discourse analyzers, and they serve as training data as well as evaluation benchmarks. Up till now, however, most of the evaluation has been done on monolithic corpora such as the Penn Treebank, the Proposition Bank. As a result, it is still unclear how the state-of-the-art analyzers perform in general on data from a variety of genres or domains. The completion of the OntoNotes corpus, a large-scale, multi-genre, multilingual corpus manually annotated with syntactic, semantic and discourse information, makes it possible to perform such an evaluation. This paper presents an analysis of the performance of publicly available, state-of-the-art tools on all layers and languages in the OntoNotes v5.0 corpus. This should set the benchmark for future development of various NLP components in syntax and semantics, and possibly encourage research towards an integrated system that makes use of the various layers jointly to improve overall performance.
MODELING BY MULTI-AGENT SYSTEMS : A SWARM INTELLIGENCE APPROACH

There are a number of emergent traffic and transportation phenomena that cannot be analyzed successfully and explained using analytical models. The only way to analyze such phenomena is through the development of models that can simulate behavior of every agent. Agent-based modeling is an approach based on the idea that a system is composed of decentralized individual ‘agents’ and that each agent interacts with other agents according to localized knowledge. The agent-based approach is a ‘bottom-up’ approach to modeling where special kinds of artificial agents are created by analogy with social insects. Social insects (including bees, wasps, ants and termites) have lived on Earth for millions of years. Their behavior in nature is primarily characterized by autonomy, distributed functioning and self-organizing capacities. Social insect colonies teach us that very simple individual organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. On the other hand, a large number of traditional engineering models and algorithms are based on control and centralization. In this article, we try to obtain the answer to the following question: Can we use some principles of natural swarm intelligence in the development of artificial systems aimed at solving complex problems in traffic and transportation?
Shifu: Deep Learning Based Advisor-advisee Relationship Mining in Scholarly Big Data

Scholars in academia are involved in various social relationships such as advisor-advisee relationships. The analysis of such relationship can provide invaluable information for understanding the interactions among scholars as well as providing many researcher-specific applications such as advisor recommendation and academic rising star identification. However, in most cases, high quality advisor-advisee relationship dataset is unavailable. To address this problem, we propose Shifu, a deep-learning-based advisor-advisee relationship identification method which takes into account both the local properties and network characteristics. In particular, we explore how to crawl advisor-advisee pairs from PhDtree project and extract their publication information by matching them with DBLP dataset as the experimental dataset. To the best of our knowledge, no prior effort has been made to address the scientific collaboration network features for relationship identification by exploiting deep learning. Our experiments demonstrate that the proposed method outperforms other state-of-the-art machine learning methods in precision (94%). Furthermore, we apply Shifu to the entire DBLP dataset and obtain a large-scale advisor-advisee relationship dataset.
Deep Transfer Learning with Joint Adaptation Networks

Deep networks have been successfully applied to learn transferable features for adapting models from a source domain to a different target domain. In this paper, we present joint adaptation networks (JAN), which learn a transfer network by aligning the joint distributions of multiple domain-specific layers across domains based on a joint maximum mean discrepancy (JMMD) criterion. Adversarial training strategy is adopted to maximize JMMD such that the distributions of the source and target domains are made more distinguishable. Learning can be performed by stochastic gradient descent with the gradients computed by back-propagation in linear-time. Experiments testify that our model yields state of the art results on standard datasets.
A Survey of High-Frequency Trading Strategies

We survey and implement a number of known high frequency trading strategies. These strategies take advantages of Thesys high frequency data and attempt to trade intraday from frequencies ranging from milliseconds to minutes, some utilizing machine learning techniques. While discussing and implementing these strategies was insightful, we fail to make significant profit after accounting for transaction costs. That is not to say these strategies do not work in general. We also hope to elucidate where our strategies fall short and where to improve them. BB contributed the correlation model strategy. DF contributed the ETF statistical arbitrage strategy. CL contributed the order book pressure strategy. MW contributed the automated technical strategy search and intelligent market making algorithm. We thank Lisa Borland and Enzo Busseti for all the insightful discussions and trading ideas. We would also like to thank Artem Vasilyev for contributing his deep insight on ”recurrent neural networks” and ”locality sensitive hashing” for instantaneous exchange latency arbitrage. Finally, Thesys provided the equity data.
Moringa oleifera Lam: Targeting Chemoprevention.

Moringa oleifera Lam, family Moringaceae, is a perennial plant which is called various names, but is locally known in Malaysia as "murungai" or "kelor". Glucomoringin, a glucosinolate with from M. oleifera is a major secondary metabolite compound. The seeds and leaves of the plant are reported to have the highest amount of glucosinolates. M. oleifera is well known for its many uses health and benefits. It is claimed to have nutritional, medicinal and chemopreventive potentials. Chemopreventive effects of M. oleifera are expected due to the existence of glucosinolate which it is reported to have the ability to induce apoptosis in anticancer studies. Furthermore, chemopreventive value of M. oleifera has been demonstrated in studies utilizing its leaf extract to inhibit the growth of human cancer cell lines. This review highlights the advantages of M. oleifera targeting chemoprevention where glucosinolates could help to slow the process of carcinogenesis through several molecular targets. It is also includes inhibition of carcinogen activation and induction of carcinogen detoxification, anti-inflammatory, anti-tumor cell proliferation, induction of apoptosis and inhibition of tumor angiogenesis. Finally, for synergistic effects of M. oleifera with other drugs and safety, essential for chemoprevention, it is important that it safe to be consumed by human body and works well. Although there is promising evidence about M. oleifera in chemoprevention, extensive research needs to be done due to the expected rise of cancer in coming years and to gain more information about the mechanisms involved in M. oleifera influence, which could be a good source to inhibit several major mechanisms involved in cancer development.