nils-herrmann/clean_scirepeval huggingface dataset:
Fifty Years of Classification and Regression Trees
Year: 2014

type="main" xml:id="insr12016 abs 0001" Fifty years have passed since the publication of the first regression tree algorithm. New techniques have added capabilities that far surpass those of the early methods. Modern classification trees can partition the data with linear splits on subsets of variables and fit nearest neighbor, kernel density, and other models in the partitions. Regression trees can fit almost every kind of traditional statistical model, including least squares, quantile, logistic, Poisson, and proportional hazards models, as well as models for longitudinal and multiresponse data. Greater availability and affordability of software (much of which is free) have played a significant role in helping the techniques gain acceptance and popularity in the broader scientific community. This article surveys the developments and briefly reviews the key ideas behind some of the major algorithms.
Scalable Cooperative Transport of Cable Suspended Loads With UAVs Using Distributed Trajectory Optimization
Venue: IEEE Robotics and Automation Letters
Year: 2020

Most approaches to multi robot control either rely on local decentralized control policies that scale well in the number of agents, or on centralized methods that can handle constraints and produce rich system level behavior, but are typically computationally expensive and scale poorly in the number of agents, relegating them to offline planning. This work presents a scalable approach that uses distributed trajectory optimization to parallelize computation over a group of computationally limited agents while handling general nonlinear dynamics and non convex constraints. The approach, including near real time onboard trajectory generation, is demonstrated in hardware on a cable suspended load problem with a team of quadrotors automatically reconfiguring to transport a heavy load through a doorway.
The Role of the Balanced Scorecard As a Tool of Strategic Management and Control
Year: 2017

Modern organizations focus on the importance of strategic management and control over current and short term goals and their corrective role on the strategic success factors of the organization in the process of using the resources of the external environment, ignoring the challenges; as the priorities of the activity are to maximally satisfy public expectations, to minimize the spending of resources, to use its own priorities, to successfully overcome uncertainty and risk, to introduce the adequacy of management decisions against the background of reasonable and measured risk. In the study alternatives are discussed as the Balanced Scorecard model suggested by observations and practice, and implications are provided in regard to changing and improving the architecture of the Balanced Scorecard (BSC) as a tool for management and control in conditions of high variability and uncertainty of the economic and social environment. Examined are some models in place to develop and implement the BSC and on this basis are structured conclusions and recommendations in the following aspects: the organization's strategy, which involves the application of BSC and the inclusion of elements to develop key aspects of the strategy; the potential of the BSC model and its application according to the different needs of management; the needs of the process of developing and implementing BSC and to further expand the applications of the BSC as a strategic management tool requiring an assessment of the expected status and priorities in the control of the indicators included.
Concepts for Reusing Composite Materials from Decommissioned Wind Turbine Blades in Affordable Housing
Year: 2018

The very rapid growth in wind energy technology in the last 15 years has led to a rapid growth in the amount of non biodegradable, thermosetting fiber reinforced polymer (FRP) composite materials used in wind turbine blades. This paper discusses conceptual architectural and structural options for recycling these blades by reusing parts of wind turbine blades in new or retrofitted housing projects. It focuses on large sized FRP pieces that can be salvaged from the turbine blades and can potentially be useful in infrastructure projects where harsh environmental conditions (water and high humidity) exist. Since reuse design should be for specific regional locations and architectural characteristics the designs presented in this paper are for the coastal regions of the Yucatan province in Mexico on the Gulf of Mexico where low quality masonry block informal housing is vulnerable to severe hurricanes and flooding. To demonstrate the concept a prototype 100 m long wind blade model developed by Sandia National Laboratories is used to show how a wind blade can be broken down into parts, thus making it possible to envision architectural applications for the different wind blade segments for housing applications.
FAST OBJECT DETECTION FRAMEWORK BASED ON MOBILENETV2 ARCHITECTURE AND ENHANCED FEATURE PYRAMID
Year: 2020

Recently, many object detectors based on deep convolutional neural networks such as Faster R CNN, SSD, RetinaNet, and so on have been proposed and showed significant improvements over traditional object detectors. However, these deep learning based object detectors usually focus on detection accuracy. This paper proposes a one stage deep learning based object detection framework to improve the inference speed and achieve real time object detection in outdoor scene images without compromising on accuracy. To improve the inference speed, this paper adopts MobileNet v2 architecture at first to generate the base convolution feature maps. MobileNet v2 achieved comparable performance compared with other state ofthe art networks while being simpler and faster. To improve the detection accuracy, an enhanced feature pyramid generation module is used to construct rich and multi scale feature maps from a single resolution input image. Each feature level is a high level semantic feature map and can be used for detecting objects at a different scale. Finally, a detection network which includes a classification subnet to predict the probability of object presence and a box regression subnet to regress the offset from each anchor box to a nearby groundtruth object is attached to each feature level in the enhanced feature pyramid to locate objects at different scales. In addition, focal loss function is used in the classification branch of the detection network to dedicate the class imbalance problems for the one stage object detector. Experimental results on public datasets show that the proposed approach achieves nearly as performance as other state of the art approaches, while the inference speed is significant improved.
Methodology for systematic analysis and improvement of manufacturing unit process life cycle inventory (UPLCI) CO2PE! initiative (cooperative effort on process emissions in manufacturing) Part 2: case studies
Venue: The International Journal of Life Cycle Assessment
Year: 2011

PurposeThis report presents two case studies, one for both the screening approach and the in depth approach, demonstrating the application of the life cycle assessment oriented methodology for systematic inventory analysis of the machine tool use phase of manufacturing unit processes, which has been developed in the framework of the CO2PE! collaborative research programme (CO2PE! 2011) and is described in part 1 of this paper (Kellens et al.  approachThe screening approach, which provides a first insight into the unit process and results in a set of approximate LCI data, relies on representative industrial data and engineering calculations for energy use and material loss. This approach is illustrated by means of a case study of a drilling process.In depth approachThe in depth approach, which leads to more accurate LCI data as well as the identification of potential for environmental improvements of the manufacturing unit processes, is subdivided into four modules, including a time study, a power consumption study, a consumables study and an emissions study, in which all relevant process in and outputs are measured and analysed in detail. The procedure of this approach, together with the proposed CO2PE! template, is illustrated by means of a case study of a laser cutting process.ResultsThe CO2PE! methodology aims to provide high quality LCI data for the machine tool use phase of manufacturing unit processes, to be used in life cycle inventory databases and libraries, as well as to identify potential for environmental improvement based on the in depth analysis of individual manufacturing unit processes. Two case studies illustrate the applicability of the methodology.
Crowdfunding, business angels, and venture capital: an exploratory study of the concept of the funding trajectory
Venue: Venture Capital
Year: 2019

The market for entrepreneurial finance has witnessed the arrival of new players specifically, different types of crowdfunding platforms which have joined the more traditional forms of entrepreneurial finance such as BAs and VCs over the last decade. Taking stock of this increasingly diverse landscape, the present study explores the concept of a complex funding trajectory and its impact on the dynamics of nascent venture governance. Using a processual case study design, we explore the particular funding trajectory of a young technology venture, successively combining different actors: reward based crowdfunding (RBC) first, then equity based crowdfunding (ECF) combined with business angels (BAs) followed by ECF combined with BAs and VCs. Our contribution to the literature is two fold. The case makes an empirical contribution by deepening insights from prior quantitative studies on the determinants of follow on funding after initial crowdfunding campaigns. The present study also makes a theoretical contribution by demonstrating that, beyond particular determinants of fundraising from various sources linked to initial venture characteristics and individual campaign attributes, the specific shape and sequencing of the overall funding trajectory plays a central role for the success of follow on funding and the nascent governance of the young venture.
La sous traitance chez Honda au Bresil quelques aspects des processus de travail, de la discrimination et de l'organisation syndicale
Year: 2008

Presentation is made of a case of subcontracting in Brazil involving the motor manufacturer Honda. The Author considers this case to be emblematic, as it is characteristic of a strong tendency in Brazil's current employment market the implementation of forms of production organization inspired by Japanese Toyotism. In Brazil, the introduction of sub contracting has brought with it increased insecurity in the workers' terms and conditions. That comes out of this report of a study of this process up to 2002. The article examines notably the emergence of conflicts between the different categories of employees who work together (from the car manufacturer and the subcontracting firm) and the negotiation of their status around the trade union organization, one of the indicators of their attachment to different categories (workers and employees)
The effect of platelet rich plasma injection in the treatment of androgenetic alopecia
Venue: Journal of cosmetic dermatology
Year: 2019

Androgenetic alopecia is the most common type of hair loss in men, which is manifested by a progressive terminal hair loss in specific areas of scalp. Platelet rich plasma (PRP) is among treatment options for androgenetic alopecia. PRP is a human platelet concentrate in a small volume of plasma containing certain cytokines.
Audio Visual Scene Aware Dialog
Venue: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2019

We introduce the task of scene aware dialog. Our goal is to generate a complete and natural response to a question about a scene, given video and audio of the scene and the history of previous turns in the dialog. To answer successfully, agents must ground concepts from the question in the video while leveraging contextual cues from the dialog history. To benchmark this task, we introduce the Audio Visual Scene Aware Dialog (AVSD) Dataset. For each of more than 11,000 videos of human actions from the Charades dataset, our dataset contains a dialog about the video, plus a final summary of the video by one of the dialog participants. We train several baseline systems for this task and evaluate the performance of the trained models using both qualitative and quantitative metrics. Our results indicate that models must utilize all the available inputs (video, audio, question, and dialog history) to perform best on this dataset.
Perilaku Keimanan, Kesabaran dan Syukur dalam Memprediksi Subjective Wellbeing Remaja
Year: 2018

Subjective wellbeing merupakan variabel yang penting bagi individu. Penelitian terdahulu menunjukkan pentingnya subjective wellbeing bagi tercapainya kualitas hidup yang optimal. Penelitian ini bertujuan untuk meneliti prediktor prediktor dari subjective wellbeing Hasil penelitian akan memberikan pemahaman tentang hubungan perilaku keimanan, kesabaran dan syukur terhadap subjective wellbeing pada mahasiswa. Penelitian ini mengunakan pendekatan kuantitatif melalui penyebaran skala. Subjek penelitian berjumlah 98 yang direkrut dari sebuah perguruan tinggi swasta (PTS) di Yogyakarta. Analisis regresi digunakan untuk menguji hubungan antar variabel. Hasil penelitian menunjukkan ada hubungan yang signifikan antara syukur dan sabar dengan subjective wellbeing pada mahasiswa. Sedangkan keimanan tidak memiliki hubungan yang signifikan dengan subjective wellbeing Implikasi dari hasil penelitian ini didiskuiskan lebih lanjut.
The desaturation method of DMSP/OLS nighttime light data based on vector data: taking the rapidly urbanized China as an example
Venue: Int. J. Geogr. Inf. Sci.
Year: 2019

The saturation of night light data caused by sensor defects conceals the differences and details of light luminance in the urban area, which greatly limits the application of Nighttime Light (NTL) in the study of urbanization. Although some methods have been proposed to mitigate the saturation of NTL data, the research of desaturation is worth further advancing due to its shortcomings in highlighting light differences and spatial resolution. Therefore, we propose a new spectral index, the Vector Data Adjusted NTL Index (VDANTLI) after analyzing the influence of different auxiliary parameters on saturation elimination. Then, we select the three most developed urban agglomerations in China and make a series of qualitative and quantitative analysis. The results of various assessments confirm that VDANTLI can effectively alleviate NTL saturation and enhance urban lighting differences. Moreover, by dynamically adjusting the length of the grid and single phase vector data modified time series NTL data, we further achieve the resolution series and time series VDANTLI. Therefore, compared with previous desaturation models, the VDANTLI can be used as an effective indicator and even an indispensable basic data for urbanization research and socio economic spatial analysis because of its unique advantages in spatial resolution and the capability in time series native NTL desaturation.
JaVerT 2.0: compositional symbolic execution for JavaScript
Venue: Proc. ACM Program. Lang.
Year: 2019

We propose a novel, unified approach to the development of compositional symbolic execution tools, bridging the gap between classical symbolic execution and compositional program reasoning based on separation logic. Using this approach, we build JaVerT 2.0, a symbolic analysis tool for JavaScript that follows the language semantics without simplifications. JaVerT 2.0 supports whole program symbolic testing, verification, and, for the first time, automatic compositional testing based on bi abduction. The meta theory underpinning JaVerT 2.0 is developed modularly, streamlining the proofs and informing the implementation. Our explicit treatment of symbolic execution errors allows us to give meaningful feedback to the developer during whole program symbolic testing and guides the inference of resource of the bi abductive execution. We evaluate the performance of JaVerT 2.0 on a number of JavaScript data structure libraries, demonstrating: the scalability of our whole program symbolic testing; an improvement over the state of the art in JavaScript verification; and the feasibility of automatic compositional testing for JavaScript.
Application of Five Fundamental Islamic Legal Maxims (al qawa'id al fiqhiyyah al khams al kubra) to Islamic Criminal Law
Year: 2020

The legal maxims of Islamic law or jurisprudential maxims or Shari'ah maxims (al qawa'id al fiqhiyyah) are the general rules of Islamic law that can be applied in various issues which come under the common rulings. This research work explores the potential of the Islamic legal maxims in contemporary Islamic legal issues and examines their role within the field of Islamic criminal law. Among the hundreds of Islamic legal maxims, the jurists have culminated the top most five maxims entitled, al qawa'id al fiqhiyyah al khams al kubra (the 5 major legal maxims) They are the universal formulas of Islamic legislature and the most comprehensive juridical principles, constituted by the jurists in accordance with the textual evidences of Shari'ah to explain specific cases and thus to participate in forming Islamic legal theories. These legal maxims have been elaborated here in this research paper along with their derivation sources and utilization in the field of criminal law of Islam. The role and application of each qaidah is supported with the help of evidences from Quran and Sunnah. The case examples are also given for a precise understanding and exploration of legal maxims in Islamic Law.
Social support and verbal interaction are differentially associated with cognitive function in midlife and older age
Venue: Neuropsychology, development, and cognition. Section B, Aging, neuropsychology and cognition
Year: 2019

Social engagement is associated with healthy aging and preserved cognition. Two dimensions of engagement, verbal interactions and perceived support, likely impact cognition via distinct mechanistic pathways. We explored the cognitive benefit of each construct among enrollees (N 1,052, mean age 60.2 years) in the Wisconsin Registry for Alzheimer's Prevention study, who provide neuropsychological and sociobehavioral data at two year intervals. Outcomes included six cognitive factor scores representing key domains of executive function and memory. Key predictors included self reported perceived social support and weekly verbal interaction. Results indicated that after adjusting for lifestyle covariates, social support was positively associated with Speed and Flexibility and that verbal interactions were associated with Verbal Learning and Memory. These findings suggest that support, which may buffer stress, and verbal interaction, an accessible, aging friendly form of environmental enrichment, are uniquely beneficial. Both are integral in the design of clinical and community interventions and programs that promote successful aging.
Frailty, Inflammation and Immunosenescence.
Venue: Interdisciplinary topics in gerontology and geriatrics
Year: 2015

Frailty is a still evolving concept of a complex phenomenon. There are several algorithms and strategies for assessing frailty syndrome, but currently, no universally accepted definition or measurement protocol has been determined. Consequently, the biological cause(s) of frailty are also poorly defined. Much circumstantial experimental data point to the dysregulation of several key physiological systems, including the neuroendocrine, musculoskeletal, metabolic and immune/inflammatory systems, resulting from alterations in functional reserves. Immune dysregulation and inflammation as causes of frailty have gained some support from the results of longitudinal studies, but a true causal relationship has not been established. This chapter will describe the immune/inflammatory alterations found in frailty and their putative causal relationships with this state.
A Vertical PRF Architecture for Microblog Search
Venue: ICTIR
Year: 2018

In microblog retrieval, query expansion can be essential to obtain good search results due to the short size of queries and posts. Since information in microblogs is highly dynamic, an up to date index coupled with pseudo relevance feedback (PRF) with an external corpus has a higher chance of retrieving more relevant documents and improving ranking. In this paper, we focus on the research question:how can we reduce the query expansion computational cost while maintaining the same retrieval precision as standard PRF? Therefore, we propose to accelerate the query expansion step of pseudo relevance feedback. The hypothesis is that using an expansion corpus organized into verticals for expanding the query, will lead to a more efficient query expansion process and improved retrieval effectiveness. Thus, the proposed query expansion method uses a distributed search architecture and resource selection algorithms to provide an efficient query expansion process. Experiments on the TREC Microblog datasets show that the proposed approach can match or outperform standard PRF in MAP and NDCG@30, with a computational cost that is three orders of magnitude lower.
Holographic Embeddings of Knowledge Graphs
Venue: AAAI
Year: 2016

Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HOLE) to learn compositional vector space representations of entire knowledge graphs. The proposed method is related to holographic models of associative memory in that it employs circular correlation to create compositional representations. By using correlation as the compositional operator, HOLE can capture rich interactions but simultaneously remains efficient to compute, easy to train, and scalable to very large datasets. Experimentally, we show that holographic embeddings are able to outperform state of the art methods for link prediction on knowledge graphs and relational learning benchmark datasets.
Sustainability Tool to Optimise Material Quantities of Steel in the Construction Industry
Year: 2018

The steel industry is arguably one of the most pollutant manufacturing sectors. The vast majority of steel produced worldwide is employed by the construction industry, mostly under the form of profile members for structural use, and it is primarily utilized in framed building structures. Even a small reduction of the steel amount currently used for building structures would therefore be beneficial, in terms of environmental impacts. This paper presents the findings from funded research, aiming to provide design practitioners with an effective optimisation tool to facilitate more material efficient structural frames to be designed, hence allowing the design community to play an active role in the ongoing 'battle' to mitigate the environmental impacts linked to the construction sector. Given a small set of required input parameters, the developed tool consents to generate a wide range of (geometrically and topologically) different steel frame designs, based on optimised cross sectional steel profiles. Overall measures of the steel mass quantities, associated with the optimised steel frame design, are then computed and outputted. In this way, the user can quantify, in an early phase of the design process, how much a chosen frame layout will affect the structural mass of the building, therefore having a design tool to explore alternative structural layout solutions, based on the range of building's shapes limitedly to the particular project at hand.