nils-herrmann/clean_scirepeval huggingface dataset:
IoT based urban climate monitoring using Raspberry Pi
Venue: 2016 International Conference on Communication and Signal Processing (ICCSP)

Internet of Things is the web of physical objects that contain the embedded technology which is helping to develop man to machine or machine to machine communication. This paper mainly propounds a stand alone system which is providing a dynamic datasheet about the parameters of the city environment. The system is using low cost low power ARM based minicomputer that is Raspberry Pi. It can communicate through Local Area Network (LAN) or the external Wi Fi module. Commands from user are processed at the Raspberry Pi using Python language. The data can be monitored with other terminal devices like Laptop, Smart Phone and Tablet which is endowed with the internet facility. This framework is giving access to real time information about an urban environment which includes the parameters: temperature, humidity, pressure, CO and harmful air pollutants.
Summarizing Multi Party Argumentative Conversations in Reader Comment on News
Venue: ArgMining@ACL

Existing approaches to summarizing multi party argumentative conversations in reader comment are extractive and fail to capture the argumentative nature of these conversations. Work on argument mining proposes schemes for identifying argument elements and relations in text but has not yet addressed how summaries might be generated from a global analysis of a conversation based on these schemes. In this paper we: (1) propose an issue centred scheme for analysing and graphically representing argument in reader comment discussion in on line news, and (2) show how summaries capturing the argumentative nature of reader comment can be generated from our graphical representation.
Intention of adoption of mobile payment: An analysis in the light of the Unified Theory of Acceptance and Use of Technology (UTAUT)

The technological improvement coupled with the growing use of smartphones has, among other functions, facilitated purchase and payment transactions through the mobile phone. This phenomenon occurs worldwide and provides individuals more flexibility and convenience in carrying out their daily activities. This article aims to evaluate the intention of adopting a future mobile payment service from the perspective of current Brazilian consumers of mobile phones, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) The survey was carried out with mobile customers of a telecommunications company that operates in southeastern Brazil, with a valid sample of 605 respondents. Using structural equation modeling, 76% of behavioral intention was explained through performance expectation, effort expectation, social influence and perceived risk. Perceived cost was found not statistically significant at the level of 5% This result serves as a guide to participants in the payments market to develop a service for mobile payments of good performance, easy to use, secure and promotes the action of the social circle of the individual at a fair price, in other words, that meets needs and expectations of today's mobile phone users. As well as serves as a stimulus to the development of communication and marketing strategies that highlight these positive attributes and awaken the intention of adoption of the service by the wider range of people as possible.
Sensitivity in error detection of patient specific QA tools for IMRT plans

The high complexity of dose calculation in treatment planning and accurate delivery of IMRT plan need high precision of verification method. The purpose of this study is to investigate error detection capability of patient specific QA tools for IMRT plans. The two H&N and two prostate IMRT plans with MapCHECK2 and portal dosimetry QA tools were studied. Measurements were undertaken for original and modified plans with errors introduced. The intentional errors composed of prescribed dose 2 to 6% and position shifting in X axis and Y axis 1 to 5mm) After measurement, gamma pass between original and modified plans were compared. The average gamma pass for original H&N and prostate plans were 98.3% and 100% for MapCHECK2 and 95.9% and 99.8% for portal dosimetry, respectively. In H&N plan, MapCHECK2 can detect position shift errors starting from 3mm while portal dosimetry can detect errors started from 2mm. Both devices showed similar sensitivity in detection of position shift error in prostate plan. For H&N plan, MapCHECK2 can detect dose errors starting at 4% whereas portal dosimetry can detect from 2% For prostate plan, both devices can identify dose errors starting from 4% Sensitivity of error detection depends on type of errors and plan complexity.
Grey wolf optimization for PID controller design with prescribed robustness margins
Venue: Soft Comput.

The grey wolf optimization algorithm is proposed to design proportional, integrative and derivative controllers using a two degrees of freedom control configuration. The control system is designed in order to achieve good set point tracking and disturbance rejection performance. The design is accomplished by minimizing an aggregated cost function based on the time weighted absolute error integral, subjected to robustness constraints. The control system robustness levels are prescribed in terms of the vector margin and maximum complementary sensitivity function values. Simulation results are presented for several common systems dynamics and compared with the ones obtained with a particle swarm optimization algorithm.
Spatial resolution and measurement uncertainty of strains in bone and bone cement interface using digital volume correlation.
Venue: Journal of the mechanical behavior of biomedical materials

The measurement uncertainty of strains has been assessed in a bone analogue (sawbone) bovine trabecular bone and bone cement interface specimens under zero load using the Digital Volume Correlation (DVC) method. The effects of sub volume size, sample constraint and preload on the measured strain uncertainty have been examined. There is generally a trade off between the measurement uncertainty and the spatial resolution. Suitable sub volume sizes have been be selected based on a compromise between the measurement uncertainty and the spatial resolution of the cases considered. A ratio of sub volume size to a microstructure characteristic (Tb.Sp) was introduced to reflect a suitable spatial resolution, and the measurement uncertainty associated was assessed. Specifically, ratios between 1.6 and 4 appear to give rise to standard deviations in the measured strains between 166 and 620 me in all the cases considered, which would seem to suffice for strain analysis in pre as well as post yield loading regimes. A microscale finite element (mFE) model was built from the CT images of the sawbone, and the results from the mFE model and a continuum FE model were compared with those from the DVC. The strain results were found to differ significantly between the two methods at tissue level, consistent in trend with the results found in human bones, indicating mainly a limitation of the current DVC method in mapping strains at this level.
Embrace Complexity Theory, Perform Contrarian Case Analysis, and Model Multiple Realities

This chapter describes tenets of complexity theory including the precept that within the same set of data X relates to Y positively, negatively, and not at all. A consequence to this first precept is that reporting how X relates positively to Y with and without additional terms in multiple regression models ignores important information available in a data set. Performing contrarian case analysis indicates that cases having low X with high Y and high X with low Y occur even when the relationship between X and Y is positive and the effect size of the relationship is large. Findings from contrarian case analysis support the necessity of modeling multiple realities using complex antecedent configurations. Complex antecedent configurations (i.e. 2 7 features per recipe) can show that high X is an indicator of high Y when high X combines with certain additional antecedent conditions (e.g. high A, high B, and low C) and low X is an indicator of high Y as well when low X combines in other recipes (e.g. high A, low R, and high S) where A, B, C, R, and S are additional antecedent conditions. Thus, modeling multiple realities configural analysis is necessary, to learn the configurations of multiple indicators for high Y outcomes and the negation of high Y. For a number of X antecedent conditions, a high X may be necessary for high Y to occur but high X alone is almost never sufficient for a high Y outcome.
GPS signal jamming and anti jamming strategy A theoretical analysis

The weak strength of GPS signal makes it very vulnerable to jamming. This calls for appropriate modeling and implementation of GPS signal source and jammers that are used to jam GPS signal. The present work describes a simple and efficient way to simulate GPS signal using MATLAB(r) with focus on investigation of GPS signal jamming and devising an anti jamming technique to counter different jamming scenarios besides Additive white Gaussian noise (AWGN) The jamming types influencing GPS Signal have been identified and suitable jamming scenarios have been modeled using MATLAB(r) The effect of each jamming signal type on GPS signal has been quantified by estimation of Bit error rate (BER) for different jamming to signal (J/S) ratio. A background study of different anti jamming techniques for GPS signal has been carried out and Turbo coding has been implemented as a suitable measure to counter the effect of GPS signal jamming.
Modeling and analyzing interference signal in a complex electromagnetic environment
Venue: EURASIP J. Wirel. Commun. Netw.

Electronic jamming is the key technology of electronic countermeasure, for which models and simulations are studied on noise jamming, deception jamming, and chaff jamming. In the noise jamming, the simulation analysis of radio frequency noise jamming is conducted and the theoretical models of other forms of noise jamming are built. The simulation analysis of range gate pull off (RGPO) is carried out in the deception jamming. In addition, theoretical modeling and simulation analysis of chaff jamming are carried out with the Monte Carlo method. Simulation results show that the models are correct and can be used to simulate the statistical characteristics of chaff jamming. With the development and wide application of electronic technology, radar, communication, navigation, and IFF signals constitute a complex electromagnetic environment. It is a must to study electronic jamming technology in the complex electromagnetic environment, which will play an indispensable role in the construction of information team and victory in the countermeasure of information technology.
Breast cancer histopathological image classification using Convolutional Neural Networks
Venue: 2016 International Joint Conference on Neural Networks (IJCNN)

The performance of most conventional classification systems relies on appropriate data representation and much of the efforts are dedicated to feature engineering, a difficult and time consuming process that uses prior expert domain knowledge of the data to create useful features. On the other hand, deep learning can extract and organize the discriminative information from the data, not requiring the design of feature extractors by a domain expert. Convolutional Neural Networks (CNNs) are a particular type of deep, feedforward network that have gained attention from research community and industry, achieving empirical successes in tasks such as speech recognition, signal processing, object recognition, natural language processing and transfer learning. In this paper, we conduct some preliminary experiments using the deep learning approach to classify breast cancer histopathological images from BreaKHis, a publicly dataset available at http:/web.inf.ufpr.br/vri/breast cancer database. We propose a method based on the extraction of image patches for training the CNN and the combination of these patches for final classification. This method aims to allow using the high resolution histopathological images from BreaKHis as input to existing CNN, avoiding adaptations of the model that can lead to a more complex and computationally costly architecture. The CNN performance is better when compared to previously reported results obtained by other machine learning models trained with hand crafted textural descriptors. Finally, we also investigate the combination of different CNNs using simple fusion rules, achieving some improvement in recognition rates.
HCP: A Flexible CNN Framework for Multi Label Image Classification
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence

Convolutional Neural Network (CNN) has demonstrated promising performance in single label image classification tasks. However, how CNN best copes with multi label images still remains an open problem, mainly due to the complex underlying object layouts and insufficient multi label training images. In this work, we propose a flexible deep CNN infrastructure, called Hypotheses CNN Pooling (HCP) where an arbitrary number of object segment hypotheses are taken as the inputs, then a shared CNN is connected with each hypothesis, and finally the CNN output results from different hypotheses are aggregated with max pooling to produce the ultimate multi label predictions. Some unique characteristics of this flexible deep CNN infrastructure include: 1) no ground truth bounding box information is required for training; 2) the whole HCP infrastructure is robust to possibly noisy and/or redundant hypotheses; 3) the shared CNN is flexible and can be well pre trained with a large scale single label image dataset, e.g. ImageNet; and 4) it may naturally output multi label prediction results. Experimental results on Pascal VOC 2007 and VOC 2012 multi label image datasets well demonstrate the superiority of the proposed HCP infrastructure over other state of the arts. In particular, the mAP reaches 90.5% by HCP only and 93.2% after the fusion with our complementary result in  based on hand crafted features on the VOC 2012 dataset.
Learning in constrained stochastic dynamic potential games
Venue: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

We extend earlier works on continuous potential games to the most general case: stochastic time varying environment, stochastic rewards, non reduced form and constrained state action sets. We provide conditions for a Markov Nash equilibrium (MNE) of the game to be equivalent to the solution of a single control problem. Then, we address the problem of learning this MNE when the reward and state transition models are unknown. We follow a reinforcement learning approach and extend previous algorithms for working with constrained state action subsets of real vector spaces. As an application example, we simulate a network flow optimization model, in which the relays have batteries that deplete with a random factor. The results obtained with the proposed framework are close to optimal.
Roles of Self Stigma, Social Support, and Positive and Negative Affects as Determinants of Depressive Symptoms Among HIV Infected Men who have Sex with Men in China
Venue: AIDS and Behavior

Poor mental health was prevalent among HIV positive men who have sex with men (HIVMSM) and a tremendous burden extents on their families and society. The present study investigated the prevalence of depression and its relationship with social support, HIV self stigma, positive affect and negative affect among 321 HIVMSM in Chengdu, China. The study was conducted during July 2013 through October 2013. Findings showed that 55.8 of the participants had mild to severe depression. The results of structural equation modeling showed that social support and positive affect were negatively associated with depression, while HIV self stigma and negative affect were positively associated with depression. Social support, positive affect, and negative affect mediated the association between HIV self stigma and depression. The hypothesized model had a satisfactory fit. Interventions improving mental health among this population are warranted.
Changes in Depressive Symptoms among Older Adults with Multiple Chronic Conditions: Role of Positive and Negative Social Support
Venue: International journal of environmental research and public health

Depression severely affects older adults in the United States. As part of the social environment, significant social support was suggested to ameliorate depression among older adults. We investigate how varying forms of social support moderate depressive symptomatology among older adults with multiple chronic conditions (MCC) Data were analyzed using a sample of 11,400 adults, aged 65 years or older, from the 2006 2012 Health and Retirement Study. The current study investigated the moderating effects of positive or negative social support from spouse, children, other family, and friends on the association between MCC and depression. A linear mixed model with repeated measures was used to estimate the effect of MCC on depression and its interactions with positive and negative social support in explaining depression among older adults. Varying forms of social support played different moderating roles in depressive symptomatology among older adults with MCC. Positive spousal support significantly weakened the deleterious effect of MCC on depression. Conversely, all negative social support from spouse, children, other family, and friends significantly strengthened the deleterious effect of MCC on depression. Minimizing negative social support and maximizing positive spousal support can reduce depression caused by MCC and lead to successful aging among older adults.
High performance carbon black counter electrodes for dye sensitized solar cells

Carbon black (CB) thin films are prepared using a doctor blade process and utilized as counter electrodes (CEs) of dye sensitized solar cells (DSSCs) Poly(vinylidene fluoride) (PVDF) is used as a binder to regulate the viscosity of the CB paste to facilitate the doctor blade process. The PVDF is then removed via thermal treatment at 350 or 450 degC. The effects of CB composition (8 15 wt% and the heat treatment temperature on the electrochemical properties of the CB electrodes are studied, as well as on the performance of the corresponding DSSCs. The results show that, after the heat treatment, all CB films demonstrate a mesoporous structure. Film thickness increases with increased CB concentration. CB films heat treated at 350 degC exhibit low electrochemical activity, high charge transfer resistance, and poor performance when utilized in DSSCs. These results are attributed to the presence of residual PVDF. By elevating the treating temperature to 450 degC, PVDF is completely removed and the electrochemical properties of the resultant CB films resemble closely those of platinum (Pt) film. The DSSCs using these CB CEs achieve conversion efficiencies (8.27 8.35% comparable to cells using Pt (8.29%
The role of various carbon nanomaterials for dye sensitized solar cells applications

This study investigates dye sensitized solar cells (DSSCs) manufactured with carbon based counter electrodes (carbon black, graphite and graphene) Specifically, it examine the effects of each commonly employed carbon allotrope on the subsequent photovoltaic performance. The fabricated carbon counter electrodes were extensively characterized by using scanning electron microscopy (SEM) X ray diffraction (XRD) Raman spectroscopy, cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) DSSCs were also manufactured using counter electrodes produced by different mixtures of carbon allotropes. It was found that the addition of graphene into carbon black produces high photovoltaic performances in DSSCs by creating additional conduction paths and a high surface area for iodide reduction. It is potentially feasible that such a carbon configuration can be used as a counter electrode, replacing the more expensive Pt.
HP Xe to go: Storage and transportation of hyperpolarized (129)Xenon.
Venue: Journal of magnetic resonance

Recently the spin lattice relaxation time T1 of hyperpolarized (HP) (129)Xe was significantly improved by using uncoated and Rb free storage vessels of GE180 glass. For these cells, a simple procedure was established to obtain reproducible wall relaxation times of about 18 h. Then the limiting relaxation mechanism in pure Xe is due to the coupling between the nuclear spins and the angular momentum of the Xe Xe van der Waals molecules. This mechanism can be significantly reduced by using different buffer gases of which CO2 was discovered to be the most efficient so far. From these values, it was estimated that for a 1:1 mixture of HP Xe with CO2 a longitudinal relaxation time of about 7 h can be expected, sufficient to transport HP Xe from a production to a remote application site. This prediction was verified for such a mixture at a total pressure of about 1 bar in a 10 cm glass cell showing a storage time of T19 h (for T1(wall)(34+ 9) h) which was transported inside a magnetic box over a distance of about 200 km by car.
Potential Importance of Ozone in the Association Between Outdoor Air Pollution and Dry Eye Disease in South Korea.
Venue: JAMA ophthalmology

Air pollution is an important public health concern and the ocular surface is continuously exposed to pollutants in outdoor air. Ocular surface abnormalities related to air pollution are thought to be a subtype of dry eye disease (DED) However, to date, there is no large scale study evaluating an association between air pollution and DED that includes multiple air pollutants.  To investigate associations between outdoor air pollution and DED in a Korean population.  A population based cross sectional study using data on 16 824 participants in the fifth Korea National Health and Nutrition Examination Survey was conducted from January 1, 2010, to December 31, 2012. Data analysis was conducted from September 1 to 30, 2015. Dry eye disease was defined as previously diagnosed by an ophthalmologist or the presence of frequent ocular pain and discomfort. Outdoor air pollution measurements (mean annual humidity, particulate matter with aerodynamic diameter <10 um [PM10] ozone, and nitrogen dioxide levels) were collected from 283 national monitoring stations in South Korea.  Sociodemographic factors and previously known factors associated with DED were applied as covariates (model 1 controlled for sociodemographic factors and model 2 controlled for sociodemographic, behavioral, and clinical factors) Results Among 16 824 participants (7104 men and 9720 women) higher ozone levels and lower humidity levels were significantly associated with symptoms and diagnosis of DED. In model 1, an increase in ozone levels of 0.003 ppm was significantly associated with symptoms and diagnosis of DED (symptoms: odds ratio [OR] 1.16; 95% CI, 1.02 1.30; P .04; diagnosis: OR, 1.21; 95% CI, 1.05 1.40; P .008) while a 5% increase in humidity levels was significantly associated with decreased symptoms and diagnoses of DED (symptoms: OR, 0.87; 95% CI, 0.77 0.98; P .03; diagnosis: OR, 0.86; 95% CI, 0.76 0.97; P .01) In model 2, an increase in ozone levels of 0.003 ppm was significantly associated with symptoms and diagnosis of DED (symptoms: OR, 1.17; 95% CI, 1.02 1.34; P .03; diagnosis: OR, 1.27; 95 CI, 1.09 1.48; P .002) while a 5% increase in humidity levels was significantly associated with decreased symptoms and diagnoses of DED (symptoms: OR, 0.88; 95% CI, 0.78 0.98; P .045; diagnosis: OR, 0.86; 95% CI, 0.76 0.97; P .02) In model 2, an increase in nitrogen dioxide of 0.003 ppm (OR, 1.12; 95% CI, 1.02 1.23 P .02) was also associated with diagnosis of DED. Levels of sulfur dioxide and PM10 were not associated with symptoms or diagnosis of DED in model 1 or model 2 (P .05 for each) Conclusions and Relevance Higher ozone levels and lower humidity levels were associated with DED in the Korean population, while PM10 level was not associated with DED.