nils-herrmann/clean_scirepeval huggingface dataset:
Continuous Online Sequence Learning with an Unsupervised Neural Network Model
Venue: Neural Computation

The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variable order temporal sequences using an unsupervised Hebbian like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods autoregressive integrated moving average; feedforward neural networks time delay neural network and online sequential extreme learning machine; and recurrent neural networks long short term memory and echo state networks on sequence prediction problems with both artificial and real world data. The HTM model achieves comparable accuracy to other state of the art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high order statistics, robustness to sensor noise and fault tolerance, and good performance without task specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real world sequence learning problems from continuous data streams.
Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings
Venue: Neural Computing and Applications

Faulty structural design may cause multistory reinforced concrete (RC) buildings to collapse suddenly. All attempts are directed to avoid structural failure as it leads to human life danger as well as wasting time and property. Using traditional methods for predicting structural failure of the RC buildings will be time consuming and complex. Recent research proved the artificial neural network (ANN) potentiality in solving various real life problems. The traditional learning algorithms suffer from being trapped into local optima with a premature convergence. Thus, it is a challenging task to achieve expected accuracy while using traditional learning algorithms to train ANN. To solve this problem, the present work proposed a particle swarm optimization based approach to train the NN (NN PSO) The PSO is employed to find a weight vector with minimum root mean square error (RMSE) for the NN. The proposed (NN PSO) classifier is capable to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. A database of 150 multistoried buildings' RC structures was employed in the experimental results. The PSO algorithm was involved to select the optimal weights for the NN classifier. Fifteen features have been extracted from the structural design, while nine features have been opted to perform the classification process. Moreover, the NN PSO model was compared with NN and MLP FFN (multilayer perceptron feed forward network) classifier to find its ingenuity. The experimental results established the superiority of the proposed NN PSO compared to the NN and MLP FFN classifiers. The NN PSO achieved 90 accuracy with 90 precision, 94.74 recall and 92.31 F Measure.
An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines
Venue: Neural Networks

In this paper, a new approach for Fault Detection and Isolation (FDI) of gas turbine engines is proposed by developing an ensemble of dynamic neural network identifiers. For health monitoring of the gas turbine engine, its dynamics is first identified by constructing three separate or individual dynamic neural network architectures. Specifically, a dynamic multi layer perceptron (MLP) a dynamic radial basis function (RBF) neural network, and a dynamic support vector machine (SVM) are trained to individually identify and represent the gas turbine engine dynamics. Next, three ensemble based techniques are developed to represent the gas turbine engine dynamics, namely, two heterogeneous ensemble models and one homogeneous ensemble model. It is first shown that all ensemble approaches do significantly improve the overall performance and accuracy of the developed system identification scheme when compared to each of the stand alone solutions. The best selected stand alone model (i.e. the dynamic RBF network) and the best selected ensemble architecture (i.e. the heterogeneous ensemble) in terms of their performances in achieving an accurate system identification are then selected for solving the FDI task. The required residual signals are generated by using both a single model based solution and an ensemble based solution under various gas turbine engine health conditions. Our extensive simulation studies demonstrate that the fault detection and isolation task achieved by using the residuals that are obtained from the dynamic ensemble scheme results in a significantly more accurate and reliable performance as illustrated through detailed quantitative confusion matrix analysis and comparative studies.
GOAliE: Goal Seeking Obstacle and Collision Evasion for Resilient Multicast Routing in Smart Grid
Venue: IEEE Transactions on Smart Grid

We present goal seeking obstacle and collision evasion, an approach to resilient multicast routing for smart grid applications based on principles from robust flocking theory. We assert that analogies exist between the flocking principles of goal seeking, obstacle evasion, collision avoidance and behavioral transitions, and the routing goals of low latency, buffer overflow management, and adaptability in the presence of changing network conditions. Our multicast routing framework employs an underlying dynamical systems model convenient for integration with representations of power system dynamics to produce an overall cyber physical smart grid description. Through simulations and comparisons with prior art we demonstrate how our approach provides insight on effective multicast routing principles to promote resilience in faulted power systems in the presence of congestion and denial of service attacks on communications infrastructure.
Do geographic concentration and vertically related variety foster firm productivity? Micro evidence from Italy

Using a large unbalanced panel dataset of more than 12,000 Italian manufacturing firms for the period 1999 2007, this paper investigates the role of geographic concentration and related variety on firms' total factor productivity (TFP) The main idea is that diversification captured by a measure of vertically related variety promotes firm innovativeness and, consequently, productivity, but when the technology has been consolidated, firms join specialized clusters that enhance efficiency. Our results suggest that the effect on firms' TFP of geographic concentration is stronger than that of related variety. We also confirm previous findings on the relevance of agglomeration forces for small firms compared with medium and large firms, where these agglomerative forces do not seem to influence TFP.
Geographic Concentration Of Home Based Medical Care Providers.
Venue: Health affairs

The United States faces a shortage of providers who care for homebound patients. About 5,000 primary care providers made 1.7 million home visits to Medicare fee for service beneficiaries in 2013, accounting for 70 percent of all home based medical visits. Nine percent of these providers performed 44 percent of visits. However, most homebound people live more than thirty miles from a high volume provider.
Comparative Review of Approved Melatonin Agonists for the Treatment of Circadian Rhythm Sleep Wake Disorders
Venue: Pharmacotherapy

Circadian rhythm sleep wake disorders (CRSWDs) are characterized by persistent or recurrent patterns of sleep disturbance related primarily to alterations of the circadian rhythm system or the misalignment between the endogenous circadian rhythm and exogenous factors that affect the timing or duration of sleep. These disorders collectively represent a significant unmet medical need, with a total prevalence in the millions, a substantial negative impact on quality of life, and a lack of studied treatments for most of these disorders. Activation of the endogenous melatonin receptors appears to play an important role in setting the circadian clock in the suprachiasmatic nucleus of the hypothalamus. Therefore, melatonin agonists, which may be able to shift and/or stabilize the circadian phase, have been identified as potential therapeutic candidates for the treatment of CRSWDs. Currently, only one melatonin receptor agonist, tasimelteon, is approved for the treatment of a CRSWD: non 24 hour sleep wake disorder (or non 24) However, three additional commercially available melatonin receptor agonists agomelatine, prolonged release melatonin, and ramelteon have been investigated for potential use for treatment of CRSWDs. Data indicate that these melatonin receptor agonists have distinct pharmacologic profiles that may help clarify their clinical use in CRSWDs. We review the pharmacokinetic and pharmacodynamic properties of these melatonin agonists and summarize their efficacy profiles when used for the treatment of CRSWDs. Further studies are needed to determine the therapeutic potential of these melatonin agonists for most CRSWDs.
Development and Effectiveness of Web Based Learning Nursing Process Program on Linked NANDA, NOC and NIC
Venue: MUE 2016

This study was done to develop and evaluate comprehensive web based learning program for improving learning ability in the nursing process of nursing students. We evaluated the contents based on the sufficient teaching materials and developed the process for implementation of the educational system. The study was focused on developing guidelines applicable to any clinical situation using a framework of links among all nursing diagnoses, outcomes, and interventions. This study was used a descriptive study and the participants were 62 nursing students. There were significant differences of achievement of learning objectives, application to work, according to the frequency of program use. Therefore, this results indicate that the web based learning nursing process program is effective in improving will help to strengthen learning calculation ability of nursing students in nursing process. Further study should be done develop additional program for improving application.
Integrating Chlorophyll fapar and Nadir Photochemical Reflectance Index from EO 1/Hyperion to Predict Cornfield Daily Gross Primary Production

The concept of light use efficiency (e) and the concept of fraction of photosynthetically active ration (PAR) absorbed for vegetation photosynthesis (PSN) i.e. fAPARPSN, have been widely utilized to estimate vegetation gross primary productivity (GPP) It has been demonstrated that the photochemical reflectance index (PRI) is empirically related to e. An experimental US Department of Agriculture (USDA) cornfield in Maryland was selected as our study field. We explored the potential of integrating fAPARchl (defined as the fraction of PAR absorbed by chlorophyll) and nadir PRI (PRInadir) to predict cornfield daily GPP. We acquired nadir or near nadir EO 1/Hyperion satellite images that covered the cornfield and took nadir in situ field spectral measurements. Those data were used to derive the PRInadir and fAPARchl. The fAPARchl is retrieved with the advanced radiative transfer model PROSAIL2 and the Metropolis approach, a type of Markov Chain Monte Carlo (MCMC) estimation procedure. We define chlorophyll light use efficiency (echl) as the ratio of vegetation GPP as measured by eddy covariance techniques to PAR absorbed by chlorophyll (echl GPP/APARchl) Daily echl retrieved with the EO 1 Hyperion images was regressed with a linear equation of PRInadir (echl a x PRInadir b) The satellite echl PRInadir linear relationship for the cornfield was implemented to develop an integrated daily GPP model [GPP (a x PRInadir b) x fAPARchl x PAR] which was evaluated with fAPARchl and PRInadir retrieved from field measurements. Daily GPP estimated with this fAPARchl PRInadir integration model was strongly correlated with the observed tower in situ daily GPP (R2 0.93) with a root mean square error (RMSE) of 1.71 g C mol 1 PPFD and coefficient of variation (CV) of 16.57% Both seasonal echl and PRInadir were strongly correlated with fAPARchl retrieved from field measurements, which indicates that chlorophyll content strongly affects seasonal echl and PRInadir. We demonstrate the potential capacity to monitor GPP with space based visible through shortwave infrared (VSWIR) imaging spectrometers such as NASA's soon to be decommissioned EO 1/Hyperion and the future Hyperspectral Infrared Imager (HyspIRI)
Examining factors influencing webinar adoption using UTAUT model (Case study at distance learning program, ABC University, Bandung Indonesia 2016)
Venue: 2016 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob)

Colleges and universities are entering a new age where the learning process is no more within the classroom. This learning is enriched by new instructional technologies and online interaction that enables the spectrum of in classroom through distance learning. Students benefit from the ability to choose the mix of course types that best fits their learning style and availability of time. The primary contribution of this research is identifying the influencing variables of students' intention on adopting webinar technology. The study was conducted by giving a questionnaire to 170 students who expressed in writing their willingness to be respondents and some of the students through deep interview. The reliability of the questionnaire is tested by the Cronbach's Alpha technique, the Validity testing by Pearson Product Moment Correlation technique using SPSS 17. The analysis technique to test the hypothesis based on Partial Least Square Structural Equation Modelling (PLS SEM) by using SmartPLS 2.0. The result shows that the propose model of performance expectancy, effort expectancy, social influence, facilitating condition, attitude toward behavior, content quality and pedagogy have strong predictive power for measuring the students' behavioral intention with R2=61% Behavioral intention has strong predictive power for measuring the students' use behavior with R2=52% Following to the results, one of the challenges of distance learning program is the distance between the students and the lecturers. The researcher believes that the distance makes the communication has bigger possibility for any distraction and the engagement between students and lecturer are lesser than in conventional class.
The effectiveness of using thieves strategy in teaching reading of recount text (an experimental research with the eighth grade students of SMP Negeri 23 Semarang in the academic year of 2015/2016)

This final project discussed The Effectiveness of Using Thieves Strategy in Teaching Reading of Recount Text (an Experimental Research with the Eighth Grade Students of SMP Negeri 23 Semarang in the Academic Year of 2015/2016) The statement of problems in this study was How is the Effectiveness of Using THIEVES Strategy in Teaching Reading of Recount Text? The background of the study is that the students of eighth grades SMP Negeri 23 Semarang have the problem to understand their English learning especially in reading. They did not interested in reading English text especially the recount text. They did not know about the meaning of the text and they do not do pronunciation well. They were still confused to identity the content of recount text. The population of the research was the eighth grade students of SMP Negeri 23 Semarang. The research method was an experimental research, which conducted in two classes; the experimental group (VIII A) and control group (VIII B) as sample. The VIII A was taught by using THIEVES Strategy, while the VIII B was taught without THIEVES Strategy. The formula to analyze the data was used t test. It was used to determine whether there was significant difference between students' score in experimental and control class or not. After collecting the data, it was found that the pre test average score in experimental class was 52.09 and control class was 56.61. Meanwhile, the post test average score of in experimental class was 75.32 and control class was 68.54 It was obtained that t test was 3.044 and the t table was 2.00 for alpha (a) 5% The t test was higher than t table (3.044>2.00) It means that Ha was accepted and Ho was rejected.Based on the result, the researcher concludes that THIEVES Strategy as a teaching strategy was effective in teaching reading recount text at SMP Negeri 23 Semarang in the Academic Year of 2015/2016.
Bayesian Non Exhaustive Classification A Case Study: Online Name Disambiguation using Temporal Record Streams
Venue: CIKM

The name entity disambiguation task aims to partition the records of multiple real life persons so that each partition contains records pertaining to a unique person. Most of the existing solutions for this task operate in a batch mode, where all records to be disambiguated are initially available to the algorithm. However, more realistic settings require that the name disambiguation task be performed in an online fashion, in addition to, being able to identify records of new ambiguous entities having no preexisting records. In this work, we propose a Bayesian non exhaustive classification framework for solving online name disambiguation task. Our proposed method uses a Dirichlet process prior with a Normal x Normal x Inverse Wishart data model which enables identification of new ambiguous entities who have no records in the training data. For online classification, we use one sweep Gibbs sampler which is very efficient and effective. As a case study we consider bibliographic data in a temporal stream format and disambiguate authors by partitioning their papers into homogeneous groups. Our experimental results demonstrate that the proposed method is better than existing methods for performing online name disambiguation task.
Supply chain social sustainability for developing nations: Evidence from India

Economic, environmental and social aspects of sustainability have been shown to span beyond organizational boundaries, indicating the importance of managing sustainability initiatives across the supply chain. Although scholars and practitioners focus a great deal of attention toward economic and environmental sustainability in supply chains, less attention is paid to social aspects. This is unfortunate, because social sustainability not only plays an important role in enabling other sustainability initiatives, but social injustices in one echelon of a supply chain can lead to significant losses for firms across the chain. Social issues have been especially problematic in developing nations, where abusive labor practices continue to negatively affect trading partners. This research seeks to disambiguate supply chain social sustainability in developing nations by uncovering relevant dimensions of social sustainability and resultant outcomes. Using semi structured interview data collected from supply chain executives in Indian manufacturing companies, this research uncovers dimensions of social sustainability in terms of not only the focal firm, but also first tier suppliers and customers. Each of these dimensions is then associated to potential performance outcomes. The findings not only provide a baseline for future research, but help practitioners understand where to focus their attention to enhance social sustainability in their supply chains.
Making the Internet of Things a reality: From smart models, sensing and actuation to energy efficient architectures
Venue: 2016 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS)

Advances in the physical sciences and engineering enable the development of internet of things (IoT) to understand, interface interact and engineer physical world (systems) However, the deployment of multitude of wireless sensors and agents spanning many application domains (e.g. environmental, healthcare, smart interconnected vehicles and trucks, smart buildings) leads not only to tremendous requirements for communicating massive amounts of multiscale heterogeneous data, but also calls for developing efficient strategies for mathematical modeling of the sensed data while overcoming the energy wall. To address these outstanding challenges, out of the box approaches need to be explored. In this tutorial paper, we discuss these outstanding problems and describe a few far reaching design methodologies that achieve the most sought after features in autonomous IoT, viz. intelligent mining, smart trustworthy sensing, closed loop (networked) control, and energy efficiency. Internet of things, autonomy, compact yet accurate models, network control, networks on chip, manycore.
How do older people describe their sensory experiences of the natural world? A systematic review of the qualitative evidence
Venue: BMC Geriatrics

BackgroundDespite the increased scholarly interest in the senses and sensory experiences, the topic of older people's sensory engagement with nature is currently under researched. This paper reviews and synthesises qualitative research evidence about how older people, including those living with dementia, describe their sensory engagement with the natural world.MethodsTen databases were searched from 1990 to September 2014: MEDLINE (Ovid) MEDLINE in Process (Ovid) PsycINFO (Ovid) CINAHL (EBSCO) GreenFILE (EBSCO) ProQuest Sociology, ASSIA (ProQuest) International Bibliography of the Social Sciences (ProQuest) HMIC (Ovid) Social Policy and Practice (Ovid) Forward and backward citation chasing of included articles was conducted; 20 organizations were contacted to identify unpublished reports. Screening was undertaken independently by two reviewers.ResultsTwenty seven studies were included. Thematic analysis revealed that descriptions of sensory experiences are encompassed within six themes: descriptions from 'the window' sensory descriptions that emphasise vision; descriptions of 'being in nature' descriptions of 'doing in nature' barriers to sensory engagement; and meanings of being and doing in nature.ConclusionsOlder people derive considerable pleasure and enjoyment from viewing nature, being and doing in nature which, in turn has a positive impact on their wellbeing and quality of life. Future research could usefully explore how sensory engagement with nature could be used to stimulate reminiscences of places and people, and evoke past sensory experiences to enrich everyday life and maintain a sense of self.