nils-herrmann/clean_scirepeval huggingface dataset:
Study on Agile Process Methodology and Emergence of Unsupervised Learning to Identify Patterns from Object Oriented System
Year: 2014

Data mining is knowledge extraction for secure software engineering, improves the quality and productivity, poses several challenges, requiring various algorithms to effectively mine text, graph from such database. Fact that building models in the context of the framework one of the task data miners, almost important though all other tasks associated with data mining. Data mining techniques are tackling the right business problem, must understand the data this is available and turn noisy data into data from which we can build robust models. It is important to be aware data mining is really what we might call an agile model. The concept of agility comes from the agile software engineering principles includes increment development of the business requirements and need to check whether the requirement satisfies with the client inputs our testing and rebuilding models improves the performance. For software engineering code execution, code changes list of bugs and requirement engineering our system uses mining techniques to explore valuable data patterns in order to meet better projects inputs and higher quality software systems that delivered on time. Our research uses frequent mining, pattern matching and machine learning applied to agile software architecture model in gathering and also extracting security requirements best effort business rules for novel research.
High Dimensional Copula Variational Approximation Through Transformation
Year: 2019

Variational methods are attractive for computing Bayesian inference when exact inference is impractical. They approximate a target distribution either the posterior or an augmented posterior using a simpler distribution that is selected to balance accuracy with computational feasibility. Here, we approximate an element wise parametric transformation of the target distribution as multivariate Gaussian or skew normal. Approximations of this kind are implicit copula models for the original parameters, with a Gaussian or skew normal copula function and flexible parametric margins. A key observation is that their adoption can improve the accuracy of variational inference in high dimensions at limited or no additional computational cost. We consider the Yeo Johnson and inverse G&H transformations, along with sparse factor structures for the scale matrix of the Gaussian or skew normal. We also show how to implement efficient reparameterization gradient methods for these copula based approximations. The efficacy of the approach is illustrated by computing posterior inference for three different models using six real datasets. In each case, we show that our proposed copula model distributions are more accurate variational approximations than Gaussian or skew normal distributions, but at only a minor or no increase in computational cost.
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Venue: ArXiv
Year: 2020

With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose spanning institutions, software, and hardware and make recommendations aimed at implementing, exploring, or improving those mechanisms.
EASL Clinical Practice Guidelines for the management of patients with decompensated cirrhosis.
Venue: Journal of hepatology
Year: 2018

The natural history of cirrhosis is characterised by an asymptomatic compensated phase followed by a decompensated phase, marked by the development of overt clinical signs, the most frequent of which are ascites, bleeding, encephalopathy, and jaundice. The following Clinical Practice Guidelines (CPGs) represent the first CPGs on the management of decompensated cirrhosis. In this context, the panel of experts, having emphasised the importance of initiating aetiologic treatment for any degree of hepatic disease at the earliest possible stage, extended its work to all the complications of cirrhosis, which had not been covered by the European Association for the Study of the Liver guidelines, namely: ascites, refractory ascites, hyponatremia, gastrointestinal bleeding, bacterial infections, acute kidney injury, hepatorenal syndrome, acute on chronic liver failure, relative adrenal failure, cirrhotic cardiomyopathy, hepatopulmonary syndrome, and porto pulmonary hypertension. The panel of experts, produced these GPGs using evidence from PubMed and Cochrane database searches providing up to date guidance on the management of decompensated cirrhosis with the only purpose of improving clinical practice.
Hippocampal Subfields Volume Reduction in High Schoolers with Previous Verbal Abuse Experiences
Venue: Clinical psychopharmacology and neuroscience the official scientific journal of the Korean College of Neuropsychopharmacology
Year: 2018

Reduced hippocampal volume and alterations in white matter tracts have been frequently reported in adults having the history of emotional maltreatment. We investigated whether these structural change occur in adolescents with previous verbal abuse (VA) experiences.  Hippocampal subfield volume and white matter structural connectivity measures were assessed in 31 first year male high school students with various degrees of exposure to parental and peer VA.  The high VA group showed significant volume reduction in the left cornu ammonis (CA) 1 and left subiculum compared to the low VA group (p<0.05) Volumes of left hippocampal subfields CA1 and subiculum were negatively correlated with previous VA experiences (p<0.05) Increased mean diffusivity (MD) of the splenium of the corpus callosum was related to high VA score across all subjects (p<0.05) There was an inverse relationship between volume of the CA1 and subiculum and MD of the splenium (p<0.05)  Exposure to parental and peer VA may affect development of the left hippocampal subfields and the splenium of corpus callosum. These structural alterations can be discernible during adolescence.
A Monte Carlo model of light propagation in human brain edema based on the visible Chinese human datasets
Year: 2018

To investigate the non invasive monitoring method of cerebral edema and the combination of cerebral edema and intracranial hemorrhage (ICH) based on fNIRS.  A head model was established based on the visible Chinese human(VCH) datasheet. The Monte Carlo simulation was conducted to analyze the light intensity, light absorption, photon fluence and spatial sensitivity distribution of head models at different levels of cerebral edema. The change in light intensity caused by ICH was also studied.  Light intensity is directly related to the increase in cerebrospinal fluid volume. Photons can be transmitted to brain tissue. The occurrence of ICH can significantly reduce light intensity and can be used to reflect the presence of ICH in cerebrospinal fluid.  The use of fNIRS technology to observe changes in light intensity is expected to be a simple and reliable bedside monitoring method for cerebral edema.  Brain edema; Functional near infrared spectroscopy; Light propagation; Monte Carlo simulation; visible Chinese human
East West: Textiles and Fashion in Early Modern Europe
Year: 2008

What is the origin and essence of fashion? This question has engaged scholars of various disciplines over the past decades, most of whom approach this subject with a Western or European focus. This paper argues instead that Asia was also pivotal in the articulation of the fashion system in Europe. The long interaction between these regions of the world initiated profound changes that included the iteration of the early modern fashion system. Silk and later printed cotton textiles are uniquely important in world history as agents of new consumer tastes, and the embodiment of fashion in Europe. Particular attention is given to the process of the Europeanization of Asian textiles, and the consideration of the intellectual, commercial and aesthetic relationship between Europe and Asia, as the European printed industry developed. Fashion was not just created through the adoption and use of Asian goods, but it was also shaped by a culture in which print was central; and it was the printing of information visual, as well as literate along with printing as a productive process, which produced a type of fashionability that could be "read"
Supervised Determined Source Separation with Multichannel Variational Autoencoder
Venue: Neural Computation
Year: 2019

This letter proposes a multichannel source separation technique, the multichannel variational autoencoder (MVAE) method, which uses a conditional VAE (CVAE) to model and estimate the power spectrograms of the sources in a mixture. By training the CVAE using the spectrograms of training examples with source class labels, we can use the trained decoder distribution as a universal generative model capable of generating spectrograms conditioned on a specified class index. By treating the latent space variables and the class index as the unknown parameters of this generative model, we can develop a convergence guaranteed algorithm for supervised determined source separation that consists of iteratively estimating the power spectrograms of the underlying sources, as well as the separation matrices. In experimental evaluations, our MVAE produced better separation performance than a baseline method.
Indicador de Tasa de politica monetaria del Banco Central de Costa Rica
Year: 2015

El presente trabajo elabora un indicador de la tasa de politica monetaria (ITPM) del Banco Central de Costa Rica (BCCR) a partir del ano 2000 en adelante. Este indicador es relevante debido a que con anterioridad a febrero del ano 2004 el BCCR no contaba de manera explicita con una tasa de politica monetaria (TPM) como instrumento para operacionalizar su estrategia monetaria y posterior a esa fecha ha variado en multiples oportunidades su definicion. Lo anterior dificulta el analisis macroeconomico en torno a esta variable por lo que resulta conveniente construir un indicador de TPM que sea coherente en el tiempo y con el esquema actual de control monetario.
EM BrA2CE v0.1: A Vocabulary and Execution Model for Declarative Business Process Modeling
Year: 2007

In management theory of the last decades, much importance has been attached to a process oriented perspective on organizational (re)structuring. Yet to date, organi zations still experience difficulties in applying this process oriented perspective to the design and maintenance information systems. The root of the problem lies with a procedural representation of business processes that contains inadequate information for computer systems to provide flexible automated business process support. The counter part of a procedural representation is a declarative one that explicitly takes into account the business concerns that govern business processes. Recently, a number of process modeling languages have appeared that could be identified as declarative languages. These modeling languages have very distinct knowledge representation backgrounds, often lack a formal execution model and often only model one aspect of the many business concerns that exist in reality. What is needed are meaningful ways to combine several kinds of expressions, called business rule types, independently of the used methods for knowledge representation and reasoning. In this paper, we present the EM BrA2CE (Enterprise Modeling using Business Rules, Agents, Activities, Concepts and Events) Framework, a unifying vocabulary and execution model for declarative process modeling. The vocabulary is described in terms of the Semantics for Business Vocabulary and Rules (SBVR) standard and the execution model is presented as a Colored Petri Net (CP Net) In addition, we show how declarative process models can contribute to the model driven design of Service Oriented Architectures.
Engineering Properties of Industrial By Products Based Controlled Low Strength Material
Year: 2020

Controlled low strength material (CLSM) also known by names such as flowable fill or controlled density fill is self compacted in nature and used in civil engineering works such as backfills, void fills, conduit bedding and many more. These materials have strength lesser than 8.3 MPa. Generally, it consists of Portland cement, fine aggregate, fly ash and water but several other materials such as foundry dust and quarry dust can also be used. In this study, the different engineering properties of CLSM with different mix proportion were evaluated. Mix proportion mainly consists of red mud having 70% 60% and 50% by weight of total composition along with fly ash of 20% 30% and 40% respectively, and phosphogypsum of 10% was studied. Portland cement was used as the binder material in all the composition in the proportion of 10% and 7% of the above composition. For different mix proportions considering flow value as an important parameter firstly percentage of water required for desired flow value (0.15 0.3 m) was evaluated by the trial method and then at those water content along with above mixture, other important engineering properties of CLSM such as compressive strength, bleeding and durability were evaluated. Except for the property of reexcavatibility (strength lesser than 2.1 MPa) for some composition all the other properties of all the composition were within the permissible limit. Overall it can be stated that red mud can be effectively used as one of the materials in CLSM.
Ongoing Intraabdominal Infection Requiring ICU Care: Prioritizing Treatment Decisions
Year: 2019

Abdominal sepsis is a frequent clinical problem in the critical care setting and is associated with significant morbidity and mortality rates. Early detection and appropriate therapeutic interventions are very important to improve clinical outcome in this condition. In this chapter, the key concepts and considerations, including technical aspects that may facilitate and optimize the timely prioritization and ongoing surgical therapy in patients with intraabdominal infections are reviewed.
Roadmap for the development of the University of North Carolina at Chapel Hill Genitourinary OncoLogy Database UNC GOLD.
Venue: Urologic oncology
Year: 2014

The management of genitourinary malignancies requires a multidisciplinary care team composed of urologists, medical oncologists, and radiation oncologists. A genitourinary (GU) oncology clinical database is an invaluable resource for patient care and research. Although electronic medical records provide a single web based record used for clinical care, billing, and scheduling, information is typically stored in a discipline specific manner and data extraction is often not applicable to a research setting. A GU oncology database may be used for the development of multidisciplinary treatment plans, analysis of disease specific practice patterns, and identification of patients for research studies. Despite the potential utility, there are many important considerations that must be addressed when developing and implementing a discipline specific database.  The creation of the GU oncology database including prostate, bladder, and kidney cancers with the identification of necessary variables was facilitated by meetings of stakeholders in medical oncology, urology, and radiation oncology at the University of North Carolina (UNC) at Chapel Hill with a template data dictionary provided by the Department of Urologic Surgery at Vanderbilt University Medical Center. Utilizing Research Electronic Data Capture (REDCap, version 4.14.5) the UNC Genitourinary OncoLogy Database (UNC GOLD) was designed and implemented.  The process of designing and implementing a discipline specific clinical database requires many important considerations. The primary consideration is determining the relationship between the database and the Institutional Review Board (IRB) given the potential applications for both clinical and research uses. Several other necessary steps include ensuring information technology security and federal regulation compliance; determination of a core complete dataset; creation of standard operating procedures; standardizing entry of free text fields; use of data exports, queries, and de identification strategies; inclusion of individual investigators' data; and strategies for prioritizing specific projects and data entry.  A discipline specific database requires a buy in from all stakeholders, meticulous development, and data entry resources to generate a unique platform for housing information that may be used for clinical care and research with IRB approval. The steps and issues identified in the development of UNC GOLD provide a process map for others interested in developing a GU oncology database.
Clinical efficacy and safety of dexmedetomidine and buprenorphine, butorphanol or diazepam for canine hip radiography.
Venue: The Journal of small animal practice
Year: 2006

To investigate the usefulness of dexmedetomidine for restraint and sedation during hip radiographic examination of hip extended or stress radiography views when combined with either buprenorphine, butorphanol or diazepam.  One hundred and twenty seven client owned clinically healthy golden retrievers or rottweilers were enrolled in a clinical trial that compared hip extended and PennHIP methods for diagnosing hip dysplasia and were randomly allocated to receive dexmedetomidine or medetomidine in combination with buprenorphine, butorphanol or diazepam. Subjective assessments were made for response to pain, response to noise, palpebral reflex, muscle tone and overall quality of sedation; non invasive physiological variables were also recorded.  Overall quality of sedation was graded as good or excellent for dogs administered with a combination of butorphanol or diazepam. However, more dogs that received a combination involving buprenorphine had overall a relatively poorer quality of sedation and required additional administration of buprenorphine before the radiographic procedure could commence. Once sedated, clinically sufficient muscle relaxation accompanied by a very low proportion of dogs responding to pain or noise stimuli were observed in all treatment groups. Heart and respiratory rate, and procedure and recovery times were similar for all treatment groups, and no adverse events were observed during the study.  Dexmedetomidine sedative protocols, particularly in combination with butorphanol and diazepam, can be used effectively and safely in dogs for radiographic procedures.
Play, Creativity, and Social Movements: If I Can't Dance, It's Not My Revolution
Year: 2011

From the Yippies to the Young Lords 2. Send in the Clowns: Play, Pleasure and Movements for Sexual Freedom 3. Play as Community Building: From Gardens to Global Peace and Justice 4. Playing in Topsy Turvery Times: From Carnival to Carnage 5. The Limits of Play: Radical Clowning vs. Tomato Picking.  Methodological Reflections on the Study of Play in Social Movements
Stochastic Relational Models for Discriminative Link Prediction
Venue: NIPS
Year: 2006

We introduce a Gaussian process (GP) framework, stochastic relational models (SRM) for learning social, physical, and other relational phenomena where interactions between entities are observed. The key idea is to model the stochastic structure of entity relationships (i.e. links) via a tensor interaction of multiple GPs, each defined on one type of entities. These models in fact define a set of nonparametric priors on infinite dimensional tensor matrices, where each element represents a relationship between a tuple of entities. By maximizing the marginalized likelihood, information is exchanged between the participating GPs through the entire relational network, so that the dependency structure of links is messaged to the dependency of entities, reflected by the adapted GP kernels. The framework offers a discriminative approach to link prediction, namely, predicting the existences, strengths, or types of relationships based on the partially observed linkage network as well as the attributes of entities (if given) We discuss properties and variants of SRM and derive an efficient learning algorithm. Very encouraging experimental results are achieved on a toy problem and a user movie preference link prediction task. In the end we discuss extensions of SRM to general relational learning tasks.
2 Dominant Resource Fairness: Fairness Efficiency Tradeoffs in Multi resource Allocation
Venue: 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC)
Year: 2018

Fair allocation has been studied intensively in both economics and computer science. Many existing mechanisms that consider fairness of resource allocation focus on a single resource. With the advance of cloud computing that centralizes multiple types of resources under one shared platform, multi resource allocation has come into the spotlight. In fact, fair/efficient multi resource allocation has become a fundamental problem in any shared computer system. The widely used solution is to partition resources into bundles that contain fixed amounts of different resources, so that multiple resources are abstracted as a single resource. However, this abstraction cannot satisfy different demands from heterogeneous users, especially on ensuring fairness among users competing for resources with different capacity limits. A promising approach to this problem is dominant resource fairness (DRF) which tries to equalize each user's dominant share (share of a user's most highly demanded resource, i.e. the largest fraction of any resource that the user has required for a task) but this method may still suffer from significant loss of efficiency (i.e. some resources are underused) This paper develops a new allocation mechanism based on DRF aiming to balance fairness and efficiency. We consider fairness not only in terms of a user's dominant resource, but also in another resource dimension which is secondarily desired by this user. We call this allocation mechanism 2 dominant resource fairness (2 DF) Then, we design a non trivial on line algorithm to find a 2 DF allocation and extend this concept to $k$ dominant resource fairness $k$ DF)