nils-herrmann/clean_scirepeval huggingface dataset:
Human centric computing The case for a Hyper Dimensional approach
Venue: 2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI)
Year: 2017

Some of most compelling application domains of the IoT and Swarm concepts relate to how humans interact with the world around it and the cyberworld beyond. While the proliferation of communication and data processing devices has profoundly altered our interaction patterns, little has been changed in the way we process inputs (sensory) and outputs (actuation) The combination of IoT (Swarms) and wearable devices offers the potential for changing all of this, opening the door for true human augmentation. The epitome of this would be a direct interface to the human brain. Yet, making sense of the plethora of information received from the often noisy sensors and making reliable decisions within very tight latency bounds 10 ms) typically demands huge computational workloads to be performed in wearable form factors at extreme energy efficiency. In this presentation, we will make the case why alternative non Von Neumann computational paradigms and architectures may be the right choice for these cognitive processing tasks. Even more, we will focus on a computational model called Hyper Dimensional Computing (HDC) and illustrate with concrete examples of why this approach may be the right one in a post Moore data driven arena.
Fundamental and innovative approaches for filler design of thermal interface materials based on epoxy resin for high power density electronics application: a retrospective
Venue: Multiscale and Multidisciplinary Modeling, Experiments and Design
Year: 2019

The journey in the packaging of microelectronics with intense heat transfer rate and miniaturization are continuing. To satisfy the above criteria, the thermal interface materials (TIMs) classified into two categories. One should have high thermal conductivity, high dielectric constant or low electrical conductivity, and high mechanical strength inclusive of processability. Other is high thermal conductivity along with electrical conductivity properties. In this review, we have reported the idea and concept behind TIMs including varieties of TIMs and their significance. We also referred to a few progressive techniques of thermally conductive filler synthesis and preparation of epoxy based thermal conductive TIMs. Some basic thermal conductivity models have been adopted to conceptualize the thermal conductivity mechanism along with various types of thermal conductivity measurement principle. We also explore how the morphological characteristics of different filler system affect the thermal conductivity of the epoxy matrix. In addition to thermal performance and efficiency, the reliability testing of TIM during package development stage is analyzed in which the design iteration has been considered for long term packages and feasibility study. The specified utility of TIMs in different electronics application has been described according to properties of fabricated composite with the high performance interface. The future perspectives of electronics packaging in advanced and miniaturized application for epoxy based TIMs are briefly elaborated.
ARIA: automatic resource inference and allocation for mapreduce environments
Venue: ICAC '11
Year: 2011

MapReduce and Hadoop represent an economically compelling alternative for efficient large scale data processing and advanced analytics in the enterprise. A key challenge in shared MapReduce clusters is the ability to automatically tailor and control resource allocations to different applications for achieving their performance goals. Currently, there is no job scheduler for MapReduce environments that given a job completion deadline, could allocate the appropriate amount of resources to the job so that it meets the required Service Level Objective (SLO) In this work, we propose a framework, called ARIA, to address this problem. It comprises of three inter related components. First, for a production job that is routinely executed on a new dataset, we build a job profile that compactly summarizes critical performance characteristics of the underlying application during the map and reduce stages. Second, we design a MapReduce performance model, that for a given job (with a known profile) and its SLO (soft deadline) estimates the amount of resources required for job completion within the deadline. Finally, we implement a novel SLO based scheduler in Hadoop that determines job ordering and the amount of resources to allocate for meeting the job deadlines. We validate our approach using a set of realistic applications. The new scheduler effectively meets the jobs' SLOs until the job demands exceed the cluster resources. The results of the extensive simulation study are validated through detailed experiments on a 66 node Hadoop cluster.
Computational Simulation of Fuel Tank Sloshing for a FSAE car using CFD Techniques
Venue: International Journal of Recent Technology and Engineering
Year: 2019

Sloshing refers to the highly random motion of any fluid inside an object where the dynamic forces of the liquid can interact with the object to alter the overall system dynamics. This work summarises the process of designing and simulating the 3 D geometry of a fuel tank using CFD and the volume of fluid (VOF) method considering multi phase fluid flow predicting fuel slosh movement at a specific capacity within a definite fixed volume.[13 16] As the performance of the engine heavily depends on a constant supply of fuel, the splashing of gasoline inside the partially filled fuel tank can severely affect the performance when subjected to sudden left and right turns during a Slalom in FSAE tracks. This scenario can be modelled, analysed and effectively controlled by reducing pressure intensities inside the tank walls using a set of strategically placed Baffles. Therefore, this study attempts to reduce the sloshing behaviour by considering multiple types of geometries and shows the final geometry chosen using computational simulations inside the fuel tank considering 1.5 litres of fuel and remaining with air inside a 7.3 litres fuel tank, thus predicting the effect of sloshing forces and moments inside the tank structure considering lateral and longitudinal acceleration fields. The model is discussed and results are presented. In addition, this paper can be referred to as a detailed tutorial on how to simulate and take in consideration of all the factors which will be useful in deciding vehicle fuel requirements and optimum design.
Capsaicin Mediates Remote Ischemic Pre Conditioning to Explain Improved Cardiovascular Mortality With Chili Pepper Intake.
Venue: Journal of the American College of Cardiology
Year: 2020

We read the recent study by Bonaccio et al. [1][1] describing the association between chili pepper consumption and decreased all cause mortality, driven primarily by cardiovascular mortality. The authors considered capsaicin's role in mediating this result through anti inflammatory, vascular,
Plagiarism in Brazil: A perspective of 25,000 PhD holders across the sciences
Year: 2019

When it comes to ownership of ideas in science, Robert K. Merton (1957) observed in Priorities in  A Chapter in the Sociology of Science that "what is true of physics, chemistry, astronomy, medicine and mathematics is true also of all the other scientific disciplines, not excluding the social and psychological sciences" However, consensus over related issues, such as what constitutes plagiarism in these fields cannot be taken for granted. We conducted a comprehensive study on plagiarism views among PhD holders registered in the database of the Brazilian National Council for Scientific and Technological Development (CNPq) We collected 25,157 valid responses encompassing views and attitudes toward plagiarism from a probability sample of PhD holders across the fields, including biologists, physicists, mathematicians, and engineers as well as linguists, philosophers and anthropologists. The results suggest that core principles about plagiarism are shared among this multidisciplinary community and that they corroborate Merton's observations. Before this study, we could only speculate that this is the case. With these data from a probability sample of Brazilian academia (PhD holders) this study offers insight into the way plagiarism is perceived across the sciences, including the literature and arts, and sheds light on the problem in the context of international collaborative research networks. The data focus on a young research system in Latin America, but, given the cultural similarities that bind most Latin American nations, these results may be relevant to other PhD populations in the region and should provide a comparison with studies from other emerging, non Anglophone regions.
Spatial Modeling of Gully Erosion Using Linear and Quadratic Discriminant Analyses in GIS and R
Year: 2019

Gully erosion is one of the most important types of water erosion that causes the destruction of agricultural and range lands in arid and semiarid areas. The main purpose of this study was to produce gully erosion susceptibility maps (GESMs) using R based data mining linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) models and to compare their performances in Shahroud Watershed, Semnan Province, Iran. The important input factors for gully erosion susceptibility assessment were obtained from different sources such as literature reviews and field surveys. First, 172 gully erosion locations were obtained using Google Earth images and extensive field surveys. Then, the gully inventory map was randomly classified into two datasets: 70% (121 gully locations) for training the models and 30% (51 gully locations) for validation purposes. Second, 12 gully erosion conditioning factors, including elevation, slope degree, slope aspect, plan curvature, distance from river, drainage density, convergence index, topography wetness index, distance from road, land use/land cover, normalized difference vegetation index, and lithology were selected. Subsequently, GESMs created using LDA and QDA models in R statistical software and they were divided into four classes including low, moderate, high, and very high. Finally, the validation dataset, which was not used in the modeling process, was considered to validate GESMs using the receiver operating characteristics curve. Results of validation showed that LDA and QDA models with area under the curve values of 0.875 and 0.8620 are good predictors for gully erosion susceptibility mapping. Also, the results indicate that in LDA and QDA models, 13.44% and 22.61% of total area is located in the very high susceptibility class to soil erosion, respectively. The outcome of this research could represent a fundamental tool for sustainable land use planning, protecting the land from water related soil erosion processes, and gully erosion hazard mitigation in the study area.
Understanding the role of green bonds in advancing sustainability
Year: 2020

Green bonds are one of the most prominent innovations in the area of sustainable finance over the past decade. However, to date there have only been a few academic studies on green bonds, and these have tended to focus on what impact green labels have on bond yields. Our analysis is one of the first empirical studies designed to address the broader questions of what attracts investors and issuers to the green bond market, the role of green bonds in shifting capital to more sustainable economic activity, and how green bonds impact the way organisations work with sustainability. Using Sweden as a case study, this paper provides insights into the rapid growth of the green bond market and how green bonds affect market participants' engagement with sustainability that are easily missed if one focuses only on how green bonds are marketed.
EERO HEINANEN A METHOD FOR AUTOMATIC TUNING OF PID CONTROLLER FOLLOWING LUUS JAAKOLA OPTIMIZATION
Year: 2018

TAMPERE UNIVERSITY OF TECHNOLOGY Automation EERO HEINANEN: A Method for automatic tuning of PID controller following Luus Jaakola optimization Master of Science Thesis, 57 pages, 7 Appendix pages October 2018 Major: Factory Automation and Industrial Informatics Examiners: Prof. Jose L. Martinez Lastra and Prof. Joni Kamarainen
TopicBERT: A Transformer transfer learning based memory graph approach for multimodal streaming social media topic detection
Venue: ArXiv
Year: 2020

Real time nature of social networks with bursty short messages and their respective large data scale spread among vast variety of topics are research interest of many researchers. These properties of social networks which are known as 5'Vs of big data has led to many unique and enlightenment algorithms and techniques applied to large social networking datasets and data streams. Many of these researches are based on detection and tracking of hot topics and trending social media events that help revealing many unanswered questions. These algorithms and in some cases software products mostly rely on the nature of the language itself. Although, other techniques such as unsupervised data mining methods are language independent but many requirements for a comprehensive solution are not met. Many research issues such as noisy sentences that adverse grammar and new online user invented words are challenging maintenance of a good social network topic detection and tracking methodology; The semantic relationship between words and in most cases, synonyms are also ignored by many of these researches. In this research, we use Transformers combined with an incremental community detection algorithm. Transformer in one hand, provides the semantic relation between words in different contexts. On the other hand, the proposed graph mining technique enhances the resulting topics with aid of simple structural rules. Named entity recognition from multimodal data, image and text, labels the named entities with entity type and the extracted topics are tuned using them. All operations of proposed system has been applied with big social data perspective under NoSQL technologies. In order to present a working and systematic solution, we combined MongoDB with Neo4j as two major database systems of our work. The proposed system shows higher precision and recall compared to other methods in three different datasets.
Does Adolescent Media Use Predict Sexual Stereotypes in Adolescence and Emerging Adulthood? Associations with Music Television and Online Pornography Exposure
Venue: Archives of sexual behavior
Year: 2020

Entertainment media consumed by adolescents have been criticized for their stereotyped depictions of sexual relationships. This longitudinal study among 182 boys and 218 girls from Belgium tests reciprocal relationships between adolescents' acceptance of gendered sexual roles and their exposure to music television and online pornography over three waves. The study innovates by including a fourth wave, approximately 5 years after Wave 3, when the participants had reached emerging adulthood, allowing to study long term associations across the two developmental stages. Results first showed that adolescents who watched more music television than their same aged peers reported a stronger acceptance of rape myths in emerging adulthood. Second, the link between adolescents' music television viewing and acceptance of rape myths in emerging adulthood was an indirect relationship through adolescents' acceptance of gendered sexual roles during adolescence. Third, adolescents' exposure to online pornography relative to their same aged peers did not predict their acceptance of gendered sexual roles or rape myths in emerging adulthood. Fourth, gender and age differences could not be investigated due to model fit problems and are suggested to be examined in future research. Implications of the long term consequences of adolescents' media use are discussed.
Online handwritten signature verification using feature weighting algorithm relief
Venue: Soft Comput.
Year: 2018

Online handwritten signatures are widely used as a reliable identity authentication technology in industries such as banking, insurance and hospitals. Most existing online handwritten signature verification schemes usually choose the same feature sets for different users while comparing different signatures, which may reduce the stability of the dynamic characteristics. To avoid this problem, we propose a novel writer dependent online signature verification technique based on Relief. For each user, our verification scheme falls into two phases: the training phase and the test phase. In the training phase, we select a signature as the base signature from real signatures and construct two different kinds of signature pairs: the real signature pairs and the forged signature pairs. Each pair of real signatures consists of two real signatures, and each pair of forgeries is made up of a real signature and a forgery. A group of combined features are defined for each pair of signatures. Then, we select more stable combined features by means of the Relief algorithm and compute a matrix used as a matching template, which contains the value of each combined feature for each pair of signatures. In the test phase, the classification of the pair of signatures composed of the test signature and the base reference signature is determined by the K nearest neighbor. Experiments are conducted on our own database and SVC2004, and the results indicate that the method we propose here shows lower FAR and FRR than do traditional methods on the same databases.
U.S. Meat Demand: Household Dynamics and Media Information Impacts
Year: 2010

This article uses national, quarterly data to examine U.S. meat demand using the Rotterdam model. We investigate the effect of multiple information indices linking different health concerns with diet, changes in household dynamics, and meat recall information. Medical journal articles linking iron, zinc, and protein with health and diet increase beef and poultry demand, whereas articles dealing with fat, cholesterol, and diet concerns reduce beef demand. Increasing consumption of food away from home enhances pork and poultry demand while reducing beef demand. Combined, these results provide a more complete and current understanding of the impact of multiple information factors faced by U.S. consumers.
Examining the microanatomy of various fetal body parts using the orcein picroindigocarmine staining method
Venue: Biotechnic histochemistry official publication of the Biological Stain Commission
Year: 2007

It is difficult to distinguish muscle, vessel, nerve, fascia, tendon and many other structures in fetal sections. The aim of the study reported here was to research the practicability of orcein picroindigocarmine staining for distinguishing anatomic structures in histological sections containing complex structures. Histological serial sections of hand, foot, head and neck of four 18 to 20 week old fetuses were used. Bone matrix was stained bright blue, collagen fibers tones of green to blue, elastic fibers brown, cartilage matrix tones of pink, and chondroblasts blue. Muscles and erythrocytes were stained yellow to green, epidermis reddish brown, skin appendices including hair and nail stained light yellow to green.
Perceptual grouping dependent lightness processing in human early visual cortex.
Venue: Journal of vision
Year: 2010

Lightness, the perceived relative achromatic reflectance of a surface, depends strongly on the context within which the surface is viewed. Modest changes in the two dimensional configuration or three dimensional scene geometry may lead to profound variations in lightness even though the surface luminance remains constant. Despite recent progress, we are far from a complete understanding of how various aspects of spatial context affect lightness processing in the cortex. Here we use a novel stimulus to show that perceptual grouping through occluders can affect lightness. We first report behavioral results showing how lightness across occlusion depends on spatially distant image features, including luminance and contrast. Next using functional magnetic resonance imaging (fMRI) we show that human early visual cortex responds strongly to occlusion dependent lightness variations with little or no attention. These results suggest that elements of three dimensional scene interpretation play a role in early cortical processing of lightness.
Cultural capital and emerging culture: the case of meditation, yoga, and vegetarianism in the UK
Year: 2020

Yoga and meditation have experienced a boom in Western appropriation in recent decades and consistently grow more culturally ubiquitous. Likewise, rates of vegetarianism are quickly rising. However, little scholarly work has been produced around these newer forms upon the cultural landscape. Even less adopts a sociological perspective. This paper seeks to remedy this fact. It aims to advance understandings as to the patterns of consumption regarding newer cultural forms, with yoga, meditation, and vegetarianism as case studies. Proceeding with the UK as the field of study, this paper presents an original national survey. It performs regression analyses with the survey data to accomplish an analysis of interaction with these cultural forms. From the results, this paper asserts that objectified cultural capital is still salient for social distinction of these forms. This paper also suggests criteria by which emerging forms of culture may establish more quickly as high status cultural objects.
Name disambiguation from link data in a collaboration graph using temporal and topological features
Venue: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)
Year: 2014

In a social community, multiple persons may share the same name, phone number or some other identifying attributes. This, along with other phenomena, such as name abbreviation, name misspelling, and human error lead to erroneous aggregation of records of multiple persons under a single reference. Such mistakes affect the performance of document retrieval, web search, database integration, and more importantly, improper attribution of credit (or blame) The task of entity disambiguation partitions the records belonging to multiple persons with the objective that each partition is composed of records of a unique person. Existing solutions to this task use either biographical attributes, or auxiliary features that are collected from external sources, such as Wikipedia. However, for many scenarios, such auxiliary features are not available, or they are costly to obtain. Besides, the attempt of collecting biographical or external data sustains the risk of privacy violation. In this work, we propose a method for solving entity disambiguation task from timestamped link information obtained from a collaboration network. Our method is non intrusive of privacy as it uses only the graph topology of an anonymized network. Experimental results on two real life academic collaboration networks show that the proposed method has satisfactory performance.
An assessment of the influence of personal branding on financing entrepreneurial ventures
Venue: Heliyon
Year: 2019

This research explores the influence of an entrepreneur's personal brand in attracting capital, by examining the validity of the Entrepreneurial Brand Personality Equity (EBPE) model of Balakrishnan and Michael (2011) Its particular concern is whether investors provide funding to an entrepreneur's idea, or, to the entrepreneur behind the idea. Concomitantly, it seeks to identify the variations in the importance accorded by different investors to the several variables of the EBPE model, and whether these variations and also the stages of business influence the final investment decisions of investors. The findings of this mixed methods study hold significant implications for various stakeholders, and suggest that the presence of the EPBE model's dimensions in an entrepreneur are very necessary for attracting investors' capital. The personal branding of the A team in particular, clearly emerged as the most critical variable of the EBPE model, based on the type of investor and stage of the entrepreneurial venture.