nils-herrmann/clean_scirepeval huggingface dataset:
Improving Event Based Non Intrusive Load Monitoring Using Graph Signal Processing
Venue: IEEE Access
Year: 2018

Large scale smart energy metering deployment worldwide and integration of smart meters within the smart grid will enable two way communication between the consumer and energy network, thus ensuring improved response to demand. Energy disaggregation or non intrusive load monitoring (NILM) namely disaggregation of the total metered electricity consumption down to individual appliances using purely algorithmic tools, is gaining popularity as an added value that makes the most of meter data. However, NILM remains a challenging problem since NILM is susceptible to sensor noise, unknown load noise, transient spikes, and fluctuations. In this paper, we tackle this problem using novel graph signal processing (GSP) concepts, applied at both, physical signal level via graph based filtering and data level, via effective semi supervised GSP based feature matching. The proposed GSP based method is generic and can be used to improve results of various event based NILM approaches. We demonstrate significant improvement in performance using three state of the art NILM methods, both supervised and unsupervised, and real world active power consumption readings from the REDD and REFIT1 data sets, sampled at 1 and 8 s, respectively.1The REFIT dataset used to generate the results can be accessed via DOI 10.15129/31da3ece f902 4e95 a093 e0a9536983c4.
Privacy Preserving Deep Learning on Machine Learning as a Service a Comprehensive Survey
Venue: IEEE Access
Year: 2020

The exponential growth of big data and deep learning has increased the data exchange traffic in society. Machine Learning as a Service, (MLaaS) which leverages deep learning techniques for predictive analytics to enhance decision making, has become a hot commodity. However, the adoption of MLaaS introduces data privacy challenges for data owners and security challenges for deep learning model owners. Data owners are concerned about the safety and privacy of their data on MLaaS platforms, while MLaaS platform owners worry that their models could be stolen by adversaries who pose as clients. Consequently, Privacy Preserving Deep Learning (PPDL) arises as a possible solution to this problem. Recently, several papers about PPDL for MLaaS have been published. However, to the best of our knowledge, no previous paper has summarized the existing literature on PPDL and its specific applicability to the MLaaS environment. In this paper, we present a comprehensive survey of privacy preserving techniques, starting from classical privacy preserving techniques to well known deep learning techniques. Additionally, we present a detailed description of PPDL and address the issue of using PPDL for MLaaS. Furthermore, we undertake detailed comparisons between state of the art PPDL methods. Subsequently, we classify an adversarial model on PPDL by highlighting possible PPDL attacks and their potential solutions. Ultimately, our paper serves as a single point of reference for detailed knowledge on PPDL and its applicability to MLaaS environments for both new and experienced researchers.
An empirical investigation of online banner ads in online market places: the cognitive factors that influence intention to click
Year: 2015

When is an internet computer user willing to click on a banner ad? This question has been addressed from the production side, via research on algorithms and models that recommend banner ads for display, as well as a few animation studies about attention grabbing features of internet advertisements. The research reported in this study takes a different approach by investigating factors that influence the intention to click online banner ads. The theoretical background for the online banner ad OBA model is the theory of reasoned action. By integrating intention to click, core factors from the technology acceptance model TAM, and antecedents specific to the banner ad context, that the OBA model explains the positive influence of important antecedents on the behavioural intention of web users to click online banner ads. To validate the OBA model, we collected data from college students. This study has significant implications for websites and apps developments.
New High Fidelity Multi physics FEM based Code to Simulate the Boron Hide out within CRUD Layer
Year: 2019

During the long term high power operation of nuclear power plant, axial offset of power distribution has occurred. CRUD, Chalk River Unidentified Deposit or Corrosion Related Unidentified Deposit, was observed in that situation on the surface of fuel rods, and boron hideout has been suggested as one of the reason for the axial shift [1,2] The lithium history in reactor coolant system during reactor operation is one of the proof for boron hideout  However, for the validation of these phenomena, more basic analysis should be conducted, as experimentally and computationally. Experimental validation for the CRUD and boron hideout is extremely hard to conduct because the boron hideout will occur at only high temperature and pressure condition with requirement of real time measurement. Computational validation can easily solve these amount of boron hideout, so modeling approach is necessary. New 2D macro scale model for solving multi physics within the CRUD layer, to predict the amount of boron hideout was developed. Finite element method (FEM) was used by using PETSc and LibMesh libraries. Heat transfer, capillary flow, solute transport and chemistry within the CRUD layer were calculated. Full core simulation system with parallel calculation was constructed with C+ language.
Twitter Sentiment Analysis of Real Time Customer Experience Feedback for Predicting Growth of Indian Telecom Companies
Venue: 2018 4th International Conference on Computing Sciences (ICCS)
Year: 2018

Corporations have always desired prompt customer experience feedback about their products for amending current pricing and policies to stay ahead of their competitors. A positive customer experience can be created by analyzing customer sentiments and acting on them promptly. Social networks like Twitter represent collective intelligence and opinion of the general public and hence can be harnessed for real time feedback. They have evolved as a resource for extracting sentiments for applications in various fields. Sentiment analysis can be used to obtain the overall customer experience of a large customer base on a real time. In this research, a total of 153,651 distinct tweets for Twitter handle of 5 popular telecom brands in India: Aircel, Bharti Airtel, Idea Cellular, Reliance Jio and Vodafone India were extracted for five months to develop a prediction model for telecom subscriber addition using the sentiment score. The results were validated statistically using correlation analysis. Positive customer sentiments about the brand which they prefer is reflected by higher growth rate of new subscribers added with that brand in the study period. The sentiment analysis results can be used by managements to take timely actions for improving the future customer experience and avoiding customer churn.
Complications Following the Mini/One Anastomosis Gastric Bypass (MGB/OAGB) a Multi institutional Survey on 2678 Patients with a Mid term (5 Years) Follow up
Venue: Obesity Surgery
Year: 2017

BackgroundIn recent years, several articles have reported considerable results with the Mini/One Anastomosis Gastric Bypass (MGB/OAGB) in terms of both weight loss and resolution of comorbidities. Despite those positive reports, some controversies still limit the widespread acceptance of this procedure. Therefore, a multicenter retrospective study, with the aim to investigate complications following this procedure, has been designed.Patients and MethodsTo report the complications rate following the MGB/OAGB and their management, and to assess the role of this approach in determining eventual complications related especially to the loop reconstruction, in the early and late postoperative periods, the clinical records of 2678 patients who underwent MGB/OAGB between 2006 and 2015 have been studied.ResultsIntraoperative and early complications rates were 0.5 and 3.1% respectively. Follow up at 5 years was 62.6% Late complications rate was 10.1% A statistical correlation was found for perioperative bleeding both with operative time (p 0.001) or a learning curve of less than 50 cases (p 0.001) A statistical correlation was found for postoperative duodenal gastro esophageal reflux (DGER) with a preexisting gastro esophageal reflux disease (GERD) or with a gastric pouch shorter than 9 cm, (p 0.001 and p 0.001) respectively. An excessive weight loss correlated with a biliopancreatic limb longer than 250 cm (p 0.001).ConclusionsOur results confirm MGB/OAGB to be a reliable bariatric procedure. According to other large and long term published series, MGB/OAGB seems to compare very favorably, in terms of complication rate, with two mainstream procedures as standard Roux en Y gastric bypass (RYGBP) and laparoscopic sleeve gastrectomy (LSG)
A common neural network architecture for visual search and working memory
Year: 2020

Visual search and working memory (WM) are tightly linked cognitive processes. Theories of attentional selection assume that WM plays an important role in top down guided visual search. However, computational models of visual search do not model WM. Here we show that an existing model of WM can utilize its mechanisms of rapid plasticity and pattern completion to perform visual search. In this model, a search template, like a memory item, is encoded into the network's synaptic weights forming a momentary stable attractor. During search, recurrent activation between the template and visual inputs amplifies the target and suppresses non matching features via mutual inhibition. While the model cannot outperform models designed specifically for search, it can, "off the shelf" account for important characteristics. Notably, it produces search display set size costs, repetition effects, and multiple template search effects, qualitatively in line with empirical data. It is also informative that the model fails to produce some important aspects of visual search behaviour, such as suppression of repeated distractors. Also, without additional control structures for top down guidance, the model lacks the ability to differentiate between encoding and searching for targets. The shared architecture bridges theories of visual search and visual WM, highlighting their common structure and their differences.
Change of Management Accounting Practices
Year: 2018

Currently, the use of traditional management accounting techniques such as traditional product costing is not meeting user needs for a variety of reasons. It has been claimed that traditional management accounting techniques are unable to satisfy the users of such techniques in terms of providing them with timely and detailed information. The use of management accounting techniques to support business processes, enabling competitive advantage and creating value is highly relevant for most businesses today. It also encourages the provision of capabilities to reduce cost and improve product quality, process flexibility, and build relationships "supplier customer" and ultimately improve performance. The research has been done by investigating the contribution of the ease of use and usefulness to accept a change in the hospital management accounting system. All the hospitals (37 hospitals) in Jakarta constituted our population. A sample for this research used 23 management accounting system changes. Our research shows that hospital management accounting system changes are acceptable. The disagreement is related with the functional issues required to support the new system. Previous researchers argue that the delegation of decision rights (decentralization) can reduce the potential for resistance of management accounting innovation implemented in two ways: (a) by creating the environment which allows managers to ensure that their subunits are able to adapt to the new signals provided by accounting innovations and (b) by enabling subunit managers to become involved in the design of these systems.
Machine Comprehension
Year: 2018

Machine Comprehension is a fundamental problem in Natural Language. A recently released dataset, the Stanford Question Answering Dataset (SQuAD) offers a large number of real questions and their answers created by humans through crowdsourcing. SQuAD provides a challenging testbed for evaluating machine comprehension algorithms, partly because compared with previous datasets, in SQuAD the answers do not come from a small set of candidate answers and they have variable lengths. In this project we implemented two models First we implemented Match LSTM model using answer pointer using boundary model. Then we also implemented another model based on Scaled Dot Product Attention and Multi Head Attention. The later model gives better results.
Determinants of Overweight and Obesity in Affluent Adolescent in Surat City, South Gujarat region, India
Venue: Indian journal of community medicine official publication of Indian Association of Preventive Social Medicine
Year: 2011

Obesity is a major global burden. Low levels of physical activity, TV watching, and dietary pattern are modifiable risk factors for overweight and obesity in adolescent.  The objective of this study was to determine risk factors for overweight and obesity among affluent adolescent, in Surat city in south Gujarat.  Cross sectional from July 2009 to April 2010.  Two private schools with tuition fees more than Rs. 2000 per month, were selected randomly using a random table.  The participants were adolescents, 12 to 15 years of age.  Pre designed and pre tested questionnaire was used to elicit the information about dietary history and physical activity.  Height and weight was measured and BMI was calculated. Overweight and obesity were assessed by BMI for age. Student who had BMI for age <85th and <95th percentile of reference population were classified as overweight and BMI for age <95th percentile of reference population were classified as obese (IAP Growth Monitoring Guidelines for Children from Birth to 18 Year)  The overall prevalence of obesity and overweight was 6.55% and 13.9% (boys: 6.7% and 15.1% girls 6.4% and 13.35% Final model of multiple logistic regression analysis showed that important determinants of overweight and obesity were low levels of physical activity, watching television or playing computer games, and consuming junk foods, snacks and carbonated drinks.  The magnitude of obesity and overweight among affluent adolescent of Surat city was found to be 6.55% and 13.9% respectively. Low level of physical activity, watching TV or playing computer games, and dietary pattern predisposed the adolescent to overweight/obesity.
Down regulation of SENCR promotes smooth muscle cells proliferation and migration in db/db mice through up regulation of FoxO1 and TRPC6.
Venue: Biomedicine pharmacotherapy Biomedecine pharmacotherapie
Year: 2015

The inappropriate proliferation of vascular smooth muscle cells (VSMCs) plays a crucial role in the atherosclerotic process. SENCR was reported to be associated with cell migration in human smooth muscle cells. However, the regulation role of SENCR in SMCs is still not fully understood.  qRT PCR and Western blotting were performed to detect the mRNA and protein levels of SENCR, FOXO1 and TRPC6 in SMCs of db/db mice and SMCs exposed to high glucose. The regulation of SENCR on the expression of FoxO1 and TRPC6 were examined with luciferase report assays. Furthermore, we investigated the effect of SENCR on VSMCs proliferation and migration using MTT assay and cell migration assay, respectively.  Here, we found that SENCR was down regulated in db/db mice and in SMCs exposed to high glucose. According to the result of luciferase assays, it was shown that SMCs knockdown enhanced the expression of FoxO1 and FoxO1 overexpression increased the expression of TRPC6. In addition, qRT PCR revealed that SENCR overexpression reversed the effect of high glucose on mouse VSMCs proliferation and migration.  In this study, our data indicated that down regulation of SENCR promoted smooth muscle cells proliferation and migration in db/db mice through up regulation of FoxO1 and TRPC6.
IMPACTS OF QUIZLET USE ON YOUNG ENGLISH LEARNERS' VOCABULARY ACQUISITION
Year: 2020

This 10 week research investigated the impacts of Quizlet, an online tool, on young English learners' vocabulary acquisition. 29 students, aged from 7 to 8, were the subjects of the research. In the first five weeks, students followed normal program without Quizlet implementation, while throughout five weeks of the second phase, Quizlet was relied on to facilitate vocabulary learning. Pretest, posttest and questionnaires were employed to collect data for analysis. The divergence between pretest and posttest results showed significant gains made by students after Quizlet was introduced. Besides, responses to the questionnaires displayed students' positive attitudes toward Quizlet use.
History of the Innovation of Damage Control for Management of Trauma Patients: 1902 2016
Venue: Annals of surgery
Year: 2017

To review the history of the innovation of damage control (DC) for management of trauma patients.  DC is an important development in trauma care that provides a valuable case study in surgical innovation.  We searched bibliographic databases (1950 2015) conference abstracts (2009 2013) Web sites, textbooks, and bibliographies for articles relating to trauma DC. The innovation of DC was then classified according to the Innovation, Development, Exploration, Assessment, and Long term study model of surgical innovation.  The "innovation" of DC originated from the use of therapeutic liver packing, a practice that had previously been abandoned after World War II because of adverse events. It then "developed" into abbreviated laparotomy using "rapid conservative operative techniques." Subsequent "exploration" resulted in the application of DC to increasingly complex abdominal injuries and thoracic, peripheral vascular, and orthopedic injuries. Increasing use of DC laparotomy was followed by growing reports of postinjury abdominal compartment syndrome and prophylactic use of the open abdomen to prevent intra abdominal hypertension after DC laparotomy. By the year 2000, DC surgery had been widely adopted and was recommended for use in surgical journals, textbooks, and teaching courses "assessment" stage of innovation) "Long term study" of DC is raising questions about whether the procedure should be used more selectively in the context of improving resuscitation practices.  The history of the innovation of DC illustrates how a previously abandoned surgical technique was adapted and readopted in response to an increased understanding of trauma patient physiology and changing injury patterns and trauma resuscitation practices.
Enhanced Gaussian process mixture model for short term electric load forecasting
Venue: Inf. Sci.
Year: 2019

This research used a hard cut iterative training algorithm to improve a Gaussian process mixture (GPM) model. Our enhanced GPM (EGPM) concisely estimates distribution parameters to the greatest extent possible. GPM models are powerful tools for data presentation and forecasting owing to their linear mix of multiple Gaussian process (GP) models. The hidden posterior probability distribution variables in the GPM model, which are based on the hard cut algorithm, are 0 and 1, respectively, which can simplify the training process and reduce calculation requirements by training each GP via a maximum likelihood estimation method. The EGPM model is then used for a short term electric load forecasting problem and compared with various forecasting models. First, the EGPM results are compared with those of two previous GPM training algorithms: the variational and leave one out cross validation (LOOCV) algorithms. The experimental results indicate that the EGPM model can accurately and more reliably forecast electric loads. The GP, support vector machine, and radial basis function network are also assessed for their ability to solve the short term electric load forecasting problem. The empirical results indicate that the performance of the proposed EGPM is superior to that of the other methods in terms of forecasting accuracy.
Pseudo transient ghost fluid methods for the Poisson Boltzmann equation with a two component regularization
Venue: Appl. Math. Comput.
Year: 2020

The Poisson Boltzmann equation (PBE) is a well established implicit solvent continuum model for the electrostatic analysis of solvated biomolecules. The numerical solution of the nonlinear PBE is still a challenge due to its exponential nonlinear term, strong singularity by the source terms, and distinct dielectric regions. In this paper, a new pseudo transient approach is proposed, which combines an analytical treatment of singular charges in a two component regularization, with an analytical integration of nonlinear term in pseudo time solution. To ensure efficiency, both fully implicit alternating direction implicit (ADI) and unconditionally stable locally one dimensional (LOD) methods have been constructed to decompose three dimensional linear systems into one dimensional (1D) ones in each pseudo time step. Moreover, to accommodate the nonzero function and flux jumps across the dielectric interface, a modified ghost fluid method (GFM) has been introduced as a first order accurate sharp interface method in 1D style, which minimizes the information needed for the molecular surface. The 1D finite difference matrix generated by the GFM is symmetric and diagonally dominant, so that the stability of ADI and LOD methods is boosted. The proposed pseudo transient GFM schemes have been numerically validated by calculating solvation free energy, binding energy, and salt effect of various proteins. It has been found that with the augmentation of regularization and GFM interface treatment, the ADI method not only enhances the accuracy dramatically, but also improves the stability significantly. By using a large time increment, an efficient protein simulation can be realized in steady state solutions. Therefore, the proposed GFM ADI and GFM LOD methods provide accurate, stable, and efficient tools for biomolecular simulations.