nils-herrmann/clean_scirepeval huggingface dataset:
Soft Nanocomposites of Nanoparticles and Nanocarbons with Supramolecular and Polymer Gels and Their Applications.
Venue: Chemical reviews
Year: 2016

Gel nanocomposites are rapidly emerging functional advanced materials having widespread applications in materials and biological sciences. Herein, we review syntheses, properties, and applications of various gel nanocomposites assembled from different metal based nanoparticles or nanocarbons [fullerene, carbon nanotubes (CNTs) and graphenes] with tailor made supramolecular (small molecular) or polymeric physical organogels and hydrogels. Dynamic supramolecular self assembly of gelators prove to be excellent hosts for the incorporation of these dimensionally different nanomaterials. Thus, gel nanocomposites doped with preformed/in situ synthesized nanoparticles show magnetic or near infrared responsive, catalytic or antibacterial properties. Fullerene based gel nanocomposites show applications in organic solar cells. Gel nanocomposites based on CNTs and graphenes and their functionalized (covalent/noncovalent) analogues find interesting properties including electrical conductivity, viscoelasticity, thermal robustness, magnetic, phase selective, redox and near infrared radiation sensitive properties. We present appropriate rationale to explain most of these phenomena at the molecular level, which provide useful perspectives for future designs and new spin offs. Finally, a possible outlook is projected for the design and syntheses of next generation multifunctional gel nanocomposites, which could be achieved by increasing the complexity of the system upon adding selective nanomaterials with desired properties in a multicomponent mixture following a de novo design in order to take advantage of their individual properties.
Evaluating deep learning architectures for Speech Emotion Recognition
Venue: Neural Networks
Year: 2017

Speech Emotion Recognition (SER) can be regarded as a static or dynamic classification problem, which makes SER an excellent test bed for investigating and comparing various deep learning architectures. We describe a frame based formulation to SER that relies on minimal speech processing and end to end deep learning to model intra utterance dynamics. We use the proposed SER system to empirically explore feed forward and recurrent neural network architectures and their variants. Experiments conducted illuminate the advantages and limitations of these architectures in paralinguistic speech recognition and emotion recognition in particular. As a result of our exploration, we report state of the art results on the IEMOCAP database for speaker independent SER and present quantitative and qualitative assessments of the models' performances.
Research and implementation of M2M smart home and security system
Venue: Secur. Commun. Networks
Year: 2015

Over recent years, the concept of Internet of Things has been put forward and undergone gradual progress. Machine to machine M2M technology, as the most prevalent application form of Internet of Things now, is widely used in various fields, among which smart home takes on tremendous market potential in virtue of its intimate relationship with human life. This paper endeavors to propose the smart home and security system, an M2M application system based on wireless networks, by combining wireless sensor network, mobile communication network, and the Internet together and briefly introduces the architecture, function, interface, identity addressing, and security mechanism of this system.
Optimal Priority Free Conditionally Preemptive Real Time Scheduling of Periodic Tasks Based on DES Supervisory Control
Venue: IEEE Transactions on Systems, Man, and Cybernetics: Systems
Year: 2017

This paper presents a general discrete event system (DES) based hard periodic real time task model. Based on supervisory control theory (SCT) an optimal priority free real time scheduling technique is proposed to process all the tasks running in uniprocessor or multiprocessor real time systems (RTS) The preemption relation in this paper generalizes priority based preemption. First, regular languages are utilized to describe the processor behavior related to each task's execution. Thereafter, the languages are represented by DES generators. Finally, the global processor behavior is generated as the synchronous product of these DES generators. By discarding the priorities, a novel preemption policy, namely conditional preemption, is developed. Two sets of conditional preemption specifications are developed, on the processor level and task level, respectively. Moreover, in order to control the system to be nonblocking and also limit the worst case response time of the tasks, two corresponding sets of specifications are presented. After generating the global specification as the synchronous product, by implementing SCT the calculated supervisor can provide all the safe real time execution sequences. The supervisor calculation can be sped up by a three step algorithm. Finally, the real time scheduling is implemented for real world examples.
Performance assessment of geopolymer concrete with partial replacement of ferrochrome slag as coarse aggregate
Venue: Construction and Building Materials
Year: 2019

The present research work aims to produce fly ash geopolymer concrete with ferrochrome slag as coarse aggregate. The experimental results of all the properties of ferrochrome slag based geopolymer concrete are compared to controlled geopolymer concrete and proved as most efficient, technically acceptable and environmentally compatible construction material. From compressive strength, flexural strength and split tensile test results, it is observed that ferrochrome slag based geopolymer concrete shows excellent performance than that of controlled geopolymer concrete. Further, scanning electron microscopy (SEM) Energy dispersive X ray analysis (EDX) Fourier transformed infrared spectroscopy (FTIR) and X ray diffraction (XRD) analyses are performed to study the behavior of microstructure with effect of different conditions.
Finite time Control for Image based Visual Servoing of a Quadrotor Using Nonsingular Fast Terminal Sliding Mode
Year: 2020

This paper investigates finite time control for image based visual servoing (IBVS) of a quadrotor subjects to image dynamics uncertainties and external disturbances. The quadrotor model is firstly proposed, based on this model and suppose that sensors on quadrotor only use a monocular camera and an inertial measurement unit, the image dynamics is developed by defining appropriate image features on virtual image plane and by means of perspective projection. The constructed visual model of the quadrotor, which includes 12 states, is decomposed into 6 subsystems in order to design the finite time controller. Then, nonsingular fast terminal sliding model (NFTSM) finite time controller is designed, and Lyapunov based finite time convergence and disturbance rejection properties are proved. Finally, simulation results are provided to demonstrate the effectiveness of the proposed results.
A fully Bayesian application of the Copas selection model for publication bias extended to network meta analysis.
Venue: Statistics in medicine
Year: 2013

The Copas parametric model is aimed at exploring the potential impact of publication bias via sensitivity analysis, by making assumptions regarding the probability of publication of individual studies related to the standard error of their effect sizes. Reviewers often have prior assumptions about the extent of selection in the set of studies included in a meta analysis. However, a Bayesian implementation of the Copas model has not been studied yet. We aim to present a Bayesian selection model for publication bias and to extend it to the case of network meta analysis where each treatment is compared either with placebo or with a reference treatment creating a star shaped network. We take advantage of the greater flexibility offered in the Bayesian context to incorporate in the model prior information on the extent and strength of selection. To derive prior distributions, we use both external data and an elicitation process of expert opinion.
Cryptocurrencies and Business Ethics
Year: 2018

Cryptocurrencies such as Bitcoin, SETLcoin, Ether, Solar Coin, or Liberty Reserve exist since 2009. Because of their decentralized control, they are often considered a threat or alternative to the conventional centralized banking system. While the technological implication of some such currencies, especially of Bitcoin, has attracted much attention, so far there is little discussion about the entire field of cryptocurrencies and very little academic literature addressing its ethical significance. In this article, we thus address the impact of "blockchain technology" on the nature of financial transactions from a business ethics perspective. We begin with a survey on relevant literature from neighboring disciplines. Next, we work towards a 3 x 3 framework for current debates on the ethics of cryptocurrencies  we combine the micro, meso, and macro levels of business and society with assessments of the potential ethical impact of cryptocurrencies as morally beneficial, detrimental, and ambiguous. In addition, we highlight possible avenues for future research, such as the changing roles of the miners and regulators, the prosocial use of cryptocurrencies, the antisocial use for shadow banking and transactions in the 'dark net' and cryptocurrencies' effect on inflation and deflation.
Brain Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives
Venue: IEEE Transactions on Biomedical Engineering
Year: 2014

Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer using brain computer interfaces (BCIs) BCI systems extract specific features of brain activity and translate them into control signals that drive an output. Recently, a category of BCIs that are built on the rhythmic activity recorded over the sensorimotor cortex, i.e. the sensorimotor rhythm (SMR) has attracted considerable attention among the BCIs that use noninvasive neural recordings, e.g. electroencephalography (EEG) and have demonstrated the capability of multidimensional prosthesis control. This paper reviews the current state and future perspectives of SMR based BCI and its clinical applications, in particular focusing on the EEG SMR. The characteristic features of SMR from the human brain are described and their underlying neural sources are discussed. The functional components of SMR based BCI, together with its current clinical applications, are reviewed. Finally, limitations of SMR BCIs and future outlooks are also discussed.
Numerical simulation of bubble dynamics in an elastic vessel
Venue: The European physical journal. E, Soft matter
Year: 2013

The nonlinear evolution of a gas bubble in the middle of an elastic vessel is investigated numerically. The fluids inside and outside the vessel are assumed to be incompressible and potential. A boundary element method (BEM) is adopted to solve the Laplace equation for the velocity potential. The gas inside the bubble is described by the Boyle Law. The fluid outside the vessel is assumed to contain the elasticity. The dynamic boundary condition on the vessel interface i is obtained from the derived Bernoulli  This numerical model is validated through the comparisons with both the analytic solution of Rayleigh Plesset equation and spark bubble experiment. The bubble dynamic behavior with different elastic parameters and vessel inner radius is further discussed.
Songs and young language learners: vocabulary and motivation
Year: 2020

The use of songs as a technique in teaching is appreciated for their linguistic, cultural, pedagogical and entertaining features. Songs are valuable learning materials which can be used to teach and develop every aspect of a language. This paper aims to review different classroom based studies focused on the effects of songs on Young Learners' vocabulary development and motivation. This review has the aim of, firstly, exploring the effectiveness of using songs as a means to improve Young Learners' English language vocabulary and, secondly, as a means of influence Young Learners' motivation towards English. Moreover, the paper also contains a theoretical framework of Young Learners in relation to both vocabulary and motivation. It also deals with how areas such as vocabulary and motivation might be related to songs. Finally, bearing in mind the findings on motivation and vocabulary, the paper includes a pedagogical proposal for an EFL class based. The analysis of the different studies show that songs have a positive influence on vocabulary of Young Learners. Furthermore, findings conclude that songs undoubtedly improve Young Learners' motivation and create a positive learning environment. Students motivated with songs feel happy, active and eager to learn.
Mindfulness A proposed operational definition
Year: 2006

There has been substantial interest in mindfulness as an approach to reduce cognitive vulnerability to stress and emotional distress in recent years. However, thus far mindfulness has not been defined operationally. This paper describes the results of recent meetings held to establish a consensus on mindfulness and to develop conjointly a testable operational definition. We propose a two component model of mindfulness and specify each component in terms of specific behaviors, experiential manifestations, and implicated psychological processes. We then address issues regarding temporal stability and situational specificity and speculate on the conceptual and operational distinctiveness of mindfulness. We conclude this paper by discussing implications for instrument development and briefly describing our own approach to measurement.
Mutational landscape and significance across 12 major cancer types
Venue: Nature
Year: 2013

The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis methods to identify somatic variants across thousands of tumours. Here we present data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types as part of the TCGA Pan Cancer effort. We illustrate the distributions of mutation frequencies, types and contexts across tumour types, and establish their links to tissues of origin, environmental/carcinogen influences, and DNA repair defects. Using the integrated data sets, we identified 127 significantly mutated genes from well known (for example, mitogen activated protein kinase, phosphatidylinositol 3 OH kinase, Wnt/b catenin and receptor tyrosine kinase signalling pathways, and cell cycle control) and emerging (for example, histone, histone modification, splicing, metabolism and proteolysis) cellular processes in cancer. The average number of mutations in these significantly mutated genes varies across tumour types; most tumours have two to six, indicating that the number of driver mutations required during oncogenesis is relatively small. Mutations in transcriptional factors/regulators show tissue specificity, whereas histone modifiers are often mutated across several cancer types. Clinical association analysis identifies genes having a significant effect on survival, and investigations of mutations with respect to clonal/subclonal architecture delineate their temporal orders during tumorigenesis. Taken together, these results lay the groundwork for developing new diagnostics and individualizing cancer treatment.
Mata merah menoreh: antologi artikel Bengkel Bahasa dan Sastra Indonesia siswa SLTA kabupaten Kulon Progo
Year: 2016

Buku antologi berjudul Mata Pena Menoreh ini adalah salah satu dari sekian banyak buku terbitan Balai Bahasa Daerah Isimewa Yogyakarta (2016) yang dimaksudkan sebagai pendukung program literasi. Buku ini berisi 38 artikel hasil proses kreatif siswa SLTA (SMA, SMK, MA) Kabupaten Kulon Progo selama mengikuti kegiatan Bengkel Bahasa dan Sastra Indonesia 2016 yang diselenggarakan oleh Balai Bahasa Daerah Istimewa Yogyakarta. Diharapkan buku ini bermanfaat bagi pembaca, khususnya para remaja sebagai generasi penerus bangsa, agar senantiasa aktif dan kreatif dalam menjaga dan menumbuhkan tradisi literasi.
Deep Metric Learning Using Triplet Network
Venue: SIMBAD
Year: 2015

Deep learning has proven itself as a successful set of models for learning useful semantic representations of data. These, however, are mostly implicitly learned as part of a classification task. In this paper we propose the triplet network model, which aims to learn useful representations by distance comparisons. A similar model was defined by Wang et al. (2014) tailor made for learning a ranking for image information retrieval. Here we demonstrate using various datasets that our model learns a better representation than that of its immediate competitor, the Siamese network. We also discuss future possible usage as a framework for unsupervised learning.
Emerging Market Economy Exchange Rates and Local Currency Bond Markets Amid the COVID 19 Pandemic
Year: 2021

Borrowing through domestic currency bonds has not insulated emerging market economies (EMEs) from the financial shock unleashed by Covid 19; EME local currency bond spreads spiked amid sharp currency depreciations and capital outflows. Portfolio investors face amplified losses as local currency spreads and exchange rates move in lockstep; their revised portfolio allocations in turn strengthen this correlation. EMEs with monetary policy frameworks that are equipped to address the feedback loop between exchange rate depreciation and capital outflows stand a better chance of weathering the financial fallout from the Covid 19 pandemic. To counter large stock adjustments in domestic bond markets, EME central banks may need to expand their toolkit to take on a "dealer of last resort" role; a number of them are already moving in this direction.
Towards Accurate Duplicate Bug Retrieval Using Deep Learning Techniques
Venue: 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME)
Year: 2017

Duplicate Bug Detection is the problem of identifying whether a newly reported bug is a duplicate of an existing bug in the system and retrieving the original or similar bugs from the past. This is required to avoid costly rediscovery and redundant work. In typical software projects, the number of duplicate bugs reported may run into the order of thousands, making it expensive in terms of cost and time for manual intervention. This makes the problem of duplicate or similar bug detection an important one in Software Engineering domain. However, an automated solution for the same is not quite accurate yet in practice, in spite of many reported approaches using various machine learning techniques. In this work, we propose a retrieval and classification model using Siamese Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) for accurate detection and retrieval of duplicate and similar bugs. We report an accuracy close to 90% and recall rate close to 80% which makes possible the practical use of such a system. We describe our model in detail along with related discussions from the Deep Learning domain. By presenting the detailed experimental results, we illustrate the effectiveness of the model in practical systems, including for repositories for which supervised training data is not available.
Analise de expressoes idiomaticas da lingua portuguesa sob a perspectiva pedagogica
Year: 2019

Em meio a tipologia das unidades do lexico, destacam se as unidades fraseologicas que, por sua vez, tambem apresentam uma tipologia propria. Dessas unidades, constituem objeto de analise neste estudo as expressoes idiomaticas, compreendidas como unidades lexicais sintagmaticas, formadas por dois ou mais elementos constituintes, indecomponiveis, cujo significado global e diferente da soma dos significados das partes componentes. Com a intencao de contribuir para o desenvolvimento da competencia lexical no ambito da sala de aula de lingua portuguesa e tendo em vista toda a riqueza cultural presente no lexico do portugues brasileiro, procuramos tratar das expressoes idiomaticas sob a perspectiva pedagogica. O objetivo deste trabalho e mostrar a analise de um conjunto de expressoes idiomaticas colhidas dos principais dicionarios escolares brasileiros. A metodologia utilizada, contando com o suporte teorico de Ferraz (2014) de Corpas Pastor (1996) e de Lewis (1993) partiu da descricao morfossintatica das expressoes idiomaticas para, numa perspectiva pedagogica, apoiada em Ferraz (2008) desenvolver a analise dos efeitos pragmaticos, decorrentes dos diversos usos de tais expressoes, envolvendo os aspectos de sinonimia e antonimia, marcas de uso, campo conceitual e estruturas semanticamente transparentes e opacas. Com isso, este trabalho procura responder as seguintes perguntas de pesquisa: i) quais aspectos relevantes para o estudo das expressoes idiomaticas em lingua portuguesa ainda estao ausentes? ii) quais possibilidades de trabalho (abordagens) com as expressoes idiomaticas, em sala de aula de lingua portuguesa, podem contribuir para o desenvolvimento da competencia lexical dos alunos brasileiros de ensino medio? O corpus de analise consiste de expressoes idiomaticas colhidas em dicionarios tipo 4, selecionados pelo PNLD Dicionarios 2012. Organizamos o corpus coletado em campos conceituais, em listas de EIs variantes, antonimas, sinonimas, coincidentes e nao coincidentes com estruturas semanticamente transparentes, subconjuntos de EIs formadas com substantivos variados e EIs que so existem na forma negativa.
Using the theory of self affirmation and self regulation strategies of mental contrasting and forming implementation intentions to reduce social anxiety symptoms
Venue: Anxiety, stress, and coping
Year: 2020

Based on the results of past research on emotion regulation and positive behavioral change via self affirmation, it was hypothesized that self affirmation should help socially anxious individuals to reduce social anxiety symptoms. The effectiveness of a brief self affirmation intervention framed in terms of implementation intentions (if then plans with self affirming cognitions) was compared against forming non affirming implementation intentions (with distraction as a way of coping) and inactive control condition. Additionally, it was tested whether mental contrasting can augment the impact of the self affirmation intervention.  Participants (N 198, aged 18 45) were randomly assigned to one of three intervention conditions (self affirming implementation intention, mental contrasting with self affirming implementation intention, or non affirming implementation intention) or an inactive control group. Social anxiety symptoms were assessed at baseline and at one week post intervention.  The difference between the interventions and the control group was substantial. However, there were no differences in the reduction of overall social anxiety levels between the interventions. Each of the interventions produced a statistically significant reduction in social anxiety (Cohen's ds from .40 to .50)  The results indicate no advantage for self affirming over non affirming implementation intentions in reducing social anxiety symptoms. Moreover, no superiority of mental contrasting was found.
Milkman's Loss of Cultural Identity in Song of Solomon
Year: 2020

Song of Solomon by Toni Morrison presents the confused, miserable, and depressed living conditions of marginalized African Americans in the society dominated by the white, focusing on the quest of Milkman Dead, the protagonist in the novel. Based on Stuart Hall's theory of cultural identity, this paper intends to probe into Milkman's loss of cultural identity by focusing on the control from the dominant white society, the loss of black culture and the alienation from family history.