nils-herrmann/clean_scirepeval huggingface dataset:
A Review on Clustering Techniques in Data Mining
Year: 2014

The main aim of Data mining process is to discover meaningful trends and patterns from the data hidden in repositories. For data analysis and data mining application, Clustering is important. It is a process or technique of grouping a set of objects that belong to the same class. Cluster analysis or Clustering has been widely used in several disciplines, such as statistics, software engineering, biology, psychology and other social sciences, in order to identify natural groups in large amounts of data. These data sets are constantly becoming larger, and their dimensionality prevents easy analysis and validation of the results. There are various clustering techniques like Simple K Means, EM, Farthest First, Filtered Clustering, Hierarchical Clustering etc. In this research work, a brief introduction to cluster analysis is given.
Efficient Multiplex Symbolic Execution with Adaptive Search Strategy
Venue: 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)
Year: 2020

Symbolic execution is still facing the scalability problem caused by path explosion and constraint solving overhead. The recently proposed MuSE framework supports exploring multiple paths by generating partial solutions in one time of solving. In this work, we improve MuSE from two aspects. Firstly, we use a light weight check to reduce redundant partial solutions for avoiding the redundant executions having the same results. Secondly, we introduce online learning to devise an adaptive search strategy for the target programs. The preliminary experimental results indicate the promising of the proposed methods.
Drop Down Menu Widget Identification Using HTML Structure Changes Classification
Venue: ACM Trans. Access. Comput.
Year: 2018

Widgets have been deployed in rich internet applications for more than 10 years. However, many of the widgets currently available on the web do not implement current accessibility design solutions standardized in ARIA (Accessible Rich Internet Applications) specification, hence are not accessible to disabled users. This article sets out an approach for automatically identifying widgets on the basis of machine learning algorithms and the classification of mutation records; it is an HTML5 technology that logs all changes that occur in the structure of a web application. Automatic widget identification is an essential component for the elaboration of automatic ARIA evaluation and adaptation strategies. Thus, the aim of this article is to take steps toward easing the software engineering process of ARIA widgets. The proposed approach focuses on the identification of drop down menu widgets. An experiment with real world web applications was conducted and the results showed evidence that this approach is capable of identifying these widgets and can outperform previous state of the art techniques based on an F measure analysis conducted during the experiment.
YIA 03 03 EFFECT OF GABAPENTIN, LIGAND OF ALPHA 2 DELTA SUBUNIT OF VOLTAGE DEPENDENT CALCIUM CHANNELS, ON BLOOD PRESSURE AND BAROREFLEX SENSITIVITY IN SPONTANEOUSLY HYPERTENSIVE RATS: THE INFLUENCE OF GUANETHIDINE INDUCED SYMPATHECTOMY
Venue: Journal of hypertension
Year: 2016

Voltage dependent calcium channels (VDCCs) play an important role in two major abnormalities observed in spontaneously hypertensive rats (SHR) hyperactivity of sympathetic nervous system (SNS) and enhanced Ca2+ influx to vascular smooth muscle cells (VSMC) &agr;2&dgr; subunit of VDCC, which promotes surface trafficking and modulates the biophysical properties of VDCC, was identified as a critical component of increased L type VDCC calcium currents in arterial myocytes of SHR. On the other hand, N type VDCCs control a neurotransmitter release in the peripheral sympathetic nervous system and play an important role in sympathetic hyperactivity in SHR.  Our study was aimed to determine the effect of &agr;2&dgr; ligand (gabapentin) on blood pressure (BP) and baroreflex sensitivity in intact or sympathectomized adult SHR and WKY rats. Baroreflex sensitivity was studied in conscious rats using increasing doses of vasoactive drugs (sodium nitroprusside and phenylephrine) The sympathectomy was induced by daily intraperitoneal injection of guanethidine for 2 weeks.  Acute gabapentin administration lowered BP more in SHR than WKY, the difference being augmented by restraint stress. Sympathetic reflex tachycardia occurred only after nifedipine (L type VDCC blocker) but not after &ohgr; conotoxin GVIA (N type VDCC blocker) or gabapentin administration. Acute gabapentin or &ohgr; conotoxin GVIA administration totally abolished the sympathetic component (without affecting the vagal component) of baroreflex sensitivity in both strains in contrast to a nonsignificant effect of nifedipine. Guanethidine induced sympathectomy lowered BP and HR in both rat strains. BP decrease after acute intravenous administration of gabapentin was prevented by sympathectomy which also abolished the effect of gabapentin on baroreflex sensitivity.  Similar effects of gabapentin and &ohgr; conotoxin GVIA suggest that acute administration of &agr;2&dgr; ligand gabapentin could reverse sympathetic hyperactivity in genetic hypertension. These results also demonstrate that cardiovascular response to gabapentin could be increased by stress induced SNS activation and diminished by sympathectomy. Partially supported by GACR_14 16225P and GACR_16 10349Y.
The effects of family control, blockholder activism, board structures and deal characteristics on acquisition performance
Year: 2018

This study examines the announcement effects and long run stock performance for acquirers from years 2000 to 2013. Since acquisitions create agency problem and companies in Malaysia exhibit concentrated ownership structures, this study aims to investigate four major objectives which consist of the effects of family control, blockholder activism, board structures and deal characteristics on stock performance of acquirers. In addressing these objectives, abnormal returns from three day before through one day after the announcements as well as abnormal returns over a 36 months period are adopted as the proxy for the announcement effects and long run stock performance respectively. Ordinary least squares regression methods are used to examine the effects of the 16 factors on abnormal returns. The results show that acquisitions in Malaysia are value enhancing, which is consistent with synergistic theory. Furthermore, family ownership and active institutional blockholders are able to create value which implies that family controlled firms do not engage in opportunistic behaviour. However, passive institutional blockholders and fairness opinion lead to lower value which indicates that these factors are unable to mitigate conflict of interest between majority and minority shareholders. As for the long run performance, Malaysia market can be considered as efficient, as most of the analyses show that the performance of acquirers do not differ from those of the matching firms. The findings imply that managers of family controlled firms do not have to worry about investors penalizing them, as long as they engage in valuecreating acquisitions. Moreover, institutional blockholders should play an active role if they want to protect their investments. Finally, investors have to realize that over the long run, there is no trading strategy that could be adopted to earn abnormal profit.
Co Governance and Sharing: The Exploration and Path Research of Social Organizations Participating in Urban Community Governance in Kunming
Year: 2020

Under the national policy and social background of innovative urban community governance mode, Guandu District, Kunming vigorously cultivates social organizations to participate in urban community service and governance, and actively promotes innovative transformation of community governance patterns. Through the investigation of urban communities, social organizations, and residents in Guandu District of Kunming City, this paper will analyze the status quo of community governance and social organizations development, as well as the exploration and paths of social organizations participating in urban community governance. It is found that there are two exploration mode and five types of participation path of Kunming social organizations which participates in urban community governance after finishing a field survey of Kunming. Only one core topic of social organization and urban community governance is indicated by these two mode and five paths: Co governance and sharing. The Chenggong exploration mode summarized in this paper is applicable to urban fringe districts, while the Guandu exploration mode is applicable to urban central urban areas. In addition, this paper explores the path of social organizations in Kunming which participates in urban community governance is aiming at providing support for the improvement of the efficiency of urban community governance, saving governance costs, and accelerating the creation of Co governance and sharing urban community in western region of China.
Improved Conservation of Energy in Fog IOT Services Using Machine Learning Model
Venue: 2020 International Conference on Computing and Information Technology (ICCIT 1441)
Year: 2020

This Fog Computing is an extension of Cloud Computing technology to the network edge, which enables a newer breed of services and applications. Resource sharing and resource discovery is considered to be critical for the performance of fog computing applications. In general, the methods to reduce are consumption of energy in heterogonous network is very minimal. Considering the higher consumption of energy in heterogeneous networks as a problem statement, this work proposes a machine learning model to reduce the consumption of energy demand in fog computing Internet of Things (IoT) services. Considering the problem, the machine learning model adopts network density, latency and mobility as its energy constraints and designs an objective function to support the lower energy consumption in the network. The simulation of the proposed method is carried out between the proposed and existing methods in terms of various performance metrics. The result shows that the proposed machine learning method in Fog IoT environment is efficient in conserving the energy than the other methods.
The protective effect of epigallocatechin gallate on renal ischemia reperfusion injury in rats
Year: 2007

To study the protective effect of epigallocatechin gallate(EGCG)on renal ischemia reperfusion injury in rats. Twelve Lewis rats were divided into 2 groups:the ex perimental group(n 7)was intravenously administered with ECCG at the dose of 10 mg/kg during the reperfusion period,and the remaining rats served as control group.At the end of reperfusion period,all the rats were sacrificed.Blood and kidney samples were collected for the blood levels of creatinine and urea nitrogen,the tissue concentration of malondialdehyde(MDA)and superoxide dis mutase(SOD)and pathological studies. The blood levels of creatinine and urea nitrogen in experimental group were lower than in control group(P0.05).The tissue concentrations of MDA and SOD in experimental group was reduced and increased,respectively,as compared with control group(P0.01).Pathological studies indicated that EGCG could attenuate the ischemia reperfusion injury in rats. EGCG is a promising agent,which can be implicated into the therapy for ischemia and reperfusion injury.
Unsupervised Cross lingual Representation Learning at Scale
Venue: ACL
Year: 2020

This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross lingual transfer tasks. We train a Transformer based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM R, significantly outperforms multilingual BERT (mBERT) on a variety of cross lingual benchmarks, including +14.6% average accuracy on XNLI, +13% average F1 score on MLQA, and +2.4% F1 score on NER. XLM R performs particularly well on low resource languages, improving 15.7% in XNLI accuracy for Swahili and 11.4% for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per language performance; XLM R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code and models publicly available.
A global local neighborhood search algorithm and tabu search for flexible job shop scheduling problem
Venue: PeerJ Comput. Sci.
Year: 2021

The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new metaheuristic algorithm called the global local neighborhood search algorithm (GLNSA) in which the neighborhood concepts of a cellular automaton are used, so that a set of leading solutions called smart cells generates and shares information that helps to optimize instances of the FJSP. The GLNSA algorithm is accompanied by a tabu search that implements a simplified version of the Nopt1 neighborhood defined in Mastrolilli Gambardella (2000) to complement the optimization task. The experiments carried out show a satisfactory performance of the proposed algorithm, compared with other results published in recent algorithms, using four benchmark sets and 101 test problems.
Low cost bus seating information technology system
Year: 2020

Public transport operators often struggle to provide a reliable and efficient transport service. A lack of comprehensive real time operational data is often cited as a major cause for this state of things. In this study, the authors report on the design, implementation and testing of an Internet of Things based system, named Bus Seating Information Technology system, which dynamically determines vehicle occupancy while the bus is in service. It uses an array of sensors for detecting events in the vehicle: infrared sensors ascertain whether passengers are entering or leaving the bus; force sensitive resistors facilitate seat occupancy detection; a Global Positioning System shield in conjunction with a Raspberry Pi microcomputer enables real time tracking of the bus; and a USB camera connected to the same Raspberry Pi assist in cross checking and validating the preceding information. The data collected is uploaded to an online IoT platform (thinger.io) through 3G or 4G if available, and can be visualised via an android app as well as through a desktop computer user interface. The planned functions of the system were tested in a 20 seater bus. Results showed that the system can track the vehicle location, as well as vehicle occupancy in real time in most cases.
Identificacion mediante ADN barcode de peces invasores en el manglar de Tumbes (Peru)
Year: 2019

El Peru es un pais megadiverso que destaca por su biodiversidad de peces, entre ellos los del manglar de Tumbes. Este ecosistema se encuentra amenazado por diversas causas, una de ellas es la introduccion de especies invasoras. La investigacion se realizo entre junio de 2016 y agosto de 2018, tuvo como objetivo identificar, mediante la tecnica de ADN barcode los peces invasores en el manglar de Tumbes. Se recolectaron peces en 18 puntos de muestreo, estos fueron identificados por sus caracteristicas morfologicas. Para la identificacion mediante ADN barcode se obtuvo sus secuencias de un fragmento del gen de la subunidad I de la citocromo oxidasa (COI) con las cuales se realizo su identificacion en las bases de datos de Blast y Bold. Tambien se realizo analisis filogenetico, construyendo arboles basados en los metodos de Neighbor joining (NJ) descubrimiento automatico de brecha de barcode (ABDG) y modelo generalizado mixto de Yule y coalescencia (GMYC) con los cuales se confirmo la asignacion de especies. De los analisis realizados se concluyo la presencia de cuatro especies invasoras: Poecilia reticulata, Oreochromis mossambicus, O. niloticus y O. urolepis.
The Best of Rules and Discretion: A Case for Nominal GDP Targeting in India
Year: 2014

The recent revival of interest in nominal GDP (NGDP) targeting has come in the context of large advanced economies. We argue that the case for NGDP targeting is even more appealing for mid sized developing countries, because they tend to be more susceptible to supply shocks and terms of trade shocks. For India, in particular, one major exogenous supply shock is the monsoon rains. NGDP targeting splits the impact of supply shocks automatically between inflation and real GDP growth. In the case of inflation targeting (IT) by contrast, the full impact of an adverse supply shock or terms of trade shock is felt as a loss in real GDP alone. NGDP targeting arguably achieves the best of both worlds: it automatically accommodates supply shocks as most central banks with discretion would do anyway, while retaining the advantage of anchoring expectations as rules are designed to do. We outline a simple theoretical model and derive the conditions under which an NGDP targeting regime would dominate other regimes such as IT for achieving objectives of output and price stability. We go on to estimate for the case of India the main parameters needed to ascertain whether these conditions hold, most notably the slope of the aggregate supply curve. We find that under certain plausible conditions, nominal GDP targeting is indeed better placed than IT, especially in the face of the supply shocks that developing countries tend to experience.
Deciphering Monetary Policy Board Minutes Through Text Mining Approach: The Case of Korea
Year: 2019

We quantify the Monetary Policy Board (MPB) minutes of the Bank of Korea (BOK) using text mining. We propose a novel approach using a field specific Korean dictionary and contiguous sequences of words (n grams) to better capture the subtlety of central bank communications. We find that our lexicon based indicators help explain the current and future BOK monetary policy decisions when considering an augmented Taylor rule, suggesting that they contain additional information beyond the currently available macroeconomic variables. Our indicators remarkably outperform English based textual classifications, a media based measure of economic policy uncertainty, and a data based measure of macroeconomic uncertainty. Our empirical results also emphasize the importance of using a field specific dictionary and the original Korean text.
Effects of ApoE4 and maternal history of dementia on hippocampal atrophy
Venue: Neurobiology of Aging
Year: 2012

We applied an automated hippocampal segmentation technique based on adaptive boosting (AdaBoost) to the 1.5 T magnetic resonance imaging (MRI) baseline and 1 year follow up data of 243 subjects with mild cognitive impairment (MCI) 96 with Alzheimer's disease (AD) and 145 normal controls (NC) scanned as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI) MCI subjects with positive maternal history of dementia had smaller hippocampal volumes at baseline and at follow up, and greater 12 month atrophy rates than subjects with negative maternal history. Three dimensional maps and volumetric multiple regression analyses demonstrated a significant effect of positive maternal history of dementia on hippocampal atrophy in MCI and AD after controlling for age, ApoE4 genotype, and paternal history of dementia, respectively. ApoE4 showed an independent effect on hippocampal atrophy in MCI and AD and in the pooled sample.
Towards a Circular Economy a Zero Waste Programme for Europe
Year: 2018

https:/doi.org/10.24264/icams 2018.XI.3 8. The first is to help cities become more sustainable. Europe is densely populated and 80 of its citizens are likely to live in or near a city by 2020. Cities often share a common set of problems such as poor air quality, high levels of noise, greenhouse gas emissions, water scarcity, and waste. The aim is to ensure that by 2020, most cities in the EU are implementing policies for sustainable urban planning and design, and are using the EU funding available for this purpose. 9. The final priority concerns wider global challenges. Many of the priority objectives in the EAP can only be achieved in cooperation with partner countries or as part of a global approach. The EU and its Member States are committed to engage more effectively in working with international partners towards the adoption of Sustainable Development Goals as a follow up to the Rio+20 conference. The EAP also proposes to explore further steps that could be taken to reduce impacts on the environment beyond EU borders. <Living well, within the limits of our planet> is a global aim.
A Study of the Morphology, Dynamics, and Folding Pathways of Ring Polymers with Supramolecular Topological Constraints Using Molecular Simulation and Nonlinear Manifold Learning
Year: 2018

Ring polymers are prevalent in natural and engineered systems, including circular bacterial DNA, crown ethers for cation chelation, and mechanical nanoswitches. The morphology and dynamics of ring polymers are governed by the chemistry and degree of polymerization of the ring and intramolecular and supramolecular topological constraints such as knots or mechanically interlocked rings. In this study, we perform molecular dynamics simulations of polyethylene ring polymers at two different degrees of polymerization and in different topological states, including a trefoil knot, catenane state (two interlocked rings) and Borromean state (three interlocked rings) We employ nonlinear manifold learning to extract the low dimensional free energy surface to which the structure and dynamics of the polymer chain are effectively restrained. The free energy surfaces reveal how the degree of polymerization and topological constraints affect the thermally accessible conformations, chiral symmetry breaking, and folding
To Tune or Not To Tune? How About the Best of Both Worlds?
Venue: ArXiv
Year: 2019

The introduction of pre trained language models has revolutionized natural language research communities. However, researchers still know relatively little regarding their theoretical and empirical properties. In this regard, Peters et al. perform several experiments which demonstrate that it is better to adapt BERT with a light weight task specific head, rather than building a complex one on top of the pre trained language model, and freeze the parameters in the said language model. However, there is another option to adopt. In this paper, we propose a new adaptation method which we first train the task model with the BERT parameters frozen and then fine tune the entire model together. Our experimental results show that our model adaptation method can achieve 4.7% accuracy improvement in semantic similarity task, 0.99% accuracy improvement in sequence labeling task and 0.72% accuracy improvement in the text classification task.