nils-herrmann/clean_scirepeval huggingface dataset:
Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion
Venue: Comput. Methods Programs Biomed.

Diabetic retinopathy is one of the leading disabling chronic diseases and one of the leading causes of preventable blindness in developed world. Early diagnosis of diabetic retinopathy enables timely treatment and in order to achieve it a major effort will have to be invested into automated population screening programs. Detection of exudates in color fundus photographs is very important for early diagnosis of diabetic retinopathy.  We use deep convolutional neural networks for exudate detection. In order to incorporate high level anatomical knowledge about potential exudate locations, output of the convolutional neural network is combined with the output of the optic disc detection and vessel detection procedures.  In the validation step using a manually segmented image database we obtain a maximum F1 measure of 0.78.  As manually segmenting and counting exudate areas is a tedious task, having a reliable automated output, such as automated segmentation using convolutional neural networks in combination with other landmark detectors, is an important step in creating automated screening programs for early detection of diabetic retinopathy.
Single loop or double loop learning: English vocabulary learning performance and behavior of students in situated computer games with different guiding strategies
Venue: Comput. Educ.

In this study, a situated computer game was implemented for engaging students in English vocabulary contexts. Furthermore, two test item guiding approaches, the cloze and multiple choice guiding strategies, were embedded in the game to serve as guidance for the students. To investigate the students' performance and behaviors of learning English vocabulary with different guiding strategies, two classes of students were included to learn with the two approaches. The experimental results showed that the students using the game with the cloze guiding strategy had significantly better learning achievement with higher cognitive load than those learning with the multiple choice guiding strategy. Moreover, from the behavioral pattern sequential analysis, it was found that the game with cloze item guidance engaged the students in both single loop situated learning (i.e. repeatedly trying to deal with the same set of learning tasks) and double loop situated learning (i.e. trying to deal with the same learning tasks after reviewing relevant materials and adjusting their learning strategies) On the other hand, those using the game with multiple choice item guidance only performed single loop situated learning during the gaming process, meaning that they seldom reviewed relevant materials or adjusted their learning strategies before trying to solve the same learning tasks again. The findings imply that it is worth considering different test item guiding approaches when developing English vocabulary games for future studies and applications. A situated computer game with two test item guiding strategies is proposed.An experiment was conducted to evaluate the effectiveness of the guiding strategies.The approaches have been applied to English vocabulary learning.The cloze guiding strategy improved the students' learning achievement.The cloze guiding strategy engaged the students in double loop learning.
Stress corrosion cracking behaviour of 7xxx aluminum alloys: A literature review

Stress corrosion cracking (SCC) is degradation of mechanical properties under the combined action of stress and corrosive environment of the susceptible material. Out of eight series of aluminium alloys, 2xxx, 5xxx and 7xxx aluminium alloys are susceptible to SCC. Among them, 7xxx series aluminium alloys have specific application in aerospace, military and structural industries due to superior mechanical properties. In these high strength 7xxx aluminium alloys, SCC plays a vital factor of consideration, as these failures are catastrophic during the service. The understanding of SCC behaviour possesses critical challenge for this alloy. The main aim of this review paper is to understand the effect of constituent alloying elements on the response of microstructural variation in various heat treated conditions on SCC behavior. Further, review was made for improving the SCC resistance using thermomechanical treatments and by surface modifications of 7xxx alloys. Apart from a brief review on SCC of 7xxx alloys, this paper presents the effect of stress and pre strain, effect of constituent alloying elements in the alloy, and the effect of environments on SCC behaviour. In addition, the SCC behaviours of weldments, 7xxx metal matrix composites and also laser surface modifications were also reviewed.
Effect of hydrogen induced plasticity on the stress corrosion cracking of X70 pipeline steel in simulated soil environments

The susceptibility to stress corrosion cracking (SCC) of X70 pipeline steel under cathodic protection in near neutral pH and acidic solutions was investigated by slow strain rate tensile test, circumferential notch tensile (CNT) test, and three point bending (TPB) test. Results confirmed the existence of a hydrogen induced plasticity (HIP) effect within a particular range of cathodic potentials. HIP effect lowered the SCC risk of X70 steel by releasing stress concentration at crack initiation spots and then decreasing the stress intensity. Crack growth behavior examined by CNT and TPB tests proved the existence of an HIP effect.
EIS analysis on stress corrosion initiation of pipeline steel under disbonded coating in near neutral pH simulated soil electrolyte

Initiation of stress corrosion of X80 high strength low alloy (HSLA) pipeline steel in a near neutral pH environment was investigated in a crevice cell simulating coating disbondment. Multi specimens were loaded simultaneously by a loading frame. EIS was applied to characterize behavior of SCC and local electrochemical process. Results indicate that the steel under disbonded coating is in an active dissolution state. In depth profile of the reciprocal charge transfer resistance shows a decreasing trend over distance from the opening. A shielding factor was proposed to characterize shielding effect of disbondment on stress corrosion of pipeline steels.
In class vs. Online Administration of Concept Inventories and Attitudinal Assessments

This study investigates differences in student responses to in class and online administrations of the Force Concept Inventory (FCI) Conceptual Survey of Electricity and Magnetism (CSEM) and the Colorado Learning Attitudes about Science Survey (CLASS) Close to 700 physics students from 12 sections of three different courses were instructed to complete the concept inventory relevant to their course, either the FCI or CSEM, and the CLASS. Each student was randomly assigned to take one of the surveys in class and the other survey online using the LA Supported Student Outcomes (LASSO) system hosted by the Learning Assistant Alliance (LAA) We examine how testing environments and instructor practices affect participation rates and identify best practices for future use.
Demographic Dialectal Variation in Social Media: A Case Study of African American English
Venue: EMNLP

Though dialectal language is increasingly abundant on social media, few resources exist for developing NLP tools to handle such language. We conduct a case study of dialectal language in online conversational text by investigating African American English (AAE) on Twitter. We propose a distantly supervised model to identify AAE like language from demographics associated with geo located messages, and we verify that this language follows well known AAE linguistic phenomena. In addition, we analyze the quality of existing language identification and dependency parsing tools on AAE like text, demonstrating that they perform poorly on such text compared to text associated with white speakers. We also provide an ensemble classifier for language identification which eliminates this disparity and release a new corpus of tweets containing AAE like language.
Effectiveness of smart training on self efficacy and self regulation in science course of Fifth Grade students of primary schools

The purpose of this study was to evaluate the effect of smart training on self efficacy and self regulation in science course of male students in the Fifth Grade of primary schools of Sabzevar.  This research was semi experimental study using pretest posttest with control group. Population included all male students in the Sixth Grade of primary schools in 2014. The sampling method in this study was a random cluster, and the sample size is equal to 56 (28 persons are present in the experimental group and 28 persons in the control group) The required data were collected using general self efficacy and Boufard's self regulation scale. The experimental group received science course by smart teaching and control group received by traditional methods. The research was carried out for one semester. SPSS software (IBM Company in USA) descriptive statistics, and covariance analysis were used to analyze data.  The results showed that there are significant differences between the scores of the two groups, and smart training increased the self efficacy and self regulation more than traditional methods.  Based on results of this research, smart schools cause the development of self efficacy and self regulation more than traditional schools, and smart schools prepare students to face mental and environmental challenges and damages by self efficacy and self regulation skills.
Preface: Special Issue on Dynamic Games in Macroeconomics
Venue: Dyn. Games Appl.

Many important questions in macroeconomics can be formulated as a dynamic game. For example, in dynamic general equilibrium models with public policy, where policymakers face a common agreed upon social objective, the optimal plans of the current government can fail to be time consistent (e.g. Kydland and Prescott  and Barro and Gordon  This time inconsistency problem can be studied as a constrained optimization problem with forwardor backward looking endogenous constraints on an enlarged state space (e.g. see Kydland and Prescott [16] Rustichini  Marcet and Marimon [21] or Feng et al.  or as a dynamic game (i.e. Pollak [26] Peleg and Yaari  Phelan and Stacchetti [24] A related situation arises when households make consumption savings decisions and their intertemporal preferences are dynamically inconsistent. This problem was first studied in Strotz [29] andPollak [26] andhas been the focus of an extensive literature inmacroeconomics (e.g. see Laibson  and Bernheim et al.  Yet another prototype dynamic game in macroeconomics arises in altruistic models of economic growth where the dynastic choice problem between generations is a strategic one. Models in this spirit were first introduced in Phelps and Pollak  and subsequently studied in Bernheim and Ray  Leininger [18] Amir  and Balbus et al.  among others. One additional prototype of a dynamic games and strategic interactions in macroeconomics occurs in models of endogenous borrowing constraints and sustainable debt (e.g. Chari and Kehoe [11] and Alvarez and Jermann  This special issue of Dynamic Games and Applications collects recent papers providing new results on the existence, characterization, and the computation of dynamic equilibria in macroeconomic models with strategically interacting agents. The volume contains work in both dynamic and stochastic games. In the paper by Messner and Pavoni [22] the authors reconsider the nature of the solutions to incentive constrained dynamic programs generated by recursive saddlepoint methods pioneered in the work of Marcet and Marimon
Punitive restoration giving the public a say on sentencing.

Mass incarceration is at near pandemic proportions and yet there is evidence of poor public confidence in how sentencing is handled by the criminal justice system with many finding the courts too lenient. This chapter considers this problem about public confidence and sentencing from a distinctive angle: if public confidence in sentencing is so poor, then why not include the public more in sentencing decision making? Restorative justice is a welcome first step about how the public can participate effectively with high satisfaction and lower reoffending at less cost. The chapter argues that restorative justice should be expanded to include more punitive outcomes, including hard treatment, in what I call "punitive restoration." More punitive options for restorative justice may increase its applicability and positive outcomes like improved public confidence while embedding it further into the criminal justice system.
A novel deep learning architecture for sentiment classification
Venue: 2016 3rd International Conference on Recent Advances in Information Technology (RAIT)

Evolution of plethora of e commerce sites resulted in fierce competition among their providers. In order to acquire new and retain existing customers, various producers and market managers effectively employ online feedback analytics tools. Most of the online feedback analysis tools are built using sentiment analysis models. Sentiment analysis evolved in the last one and half decades for review mining process. An important sub task of sentiment analysis called sentiment classification is used mainly to decide whether a written review is expressing either positive or negative sentiment towards a target entity. In order to have better sentiment classification accuracy, we proposed a hybrid deep learning architecture, which is a hybrid of a two layered Restricted Boltzmann Machine and a Probabilistic Neural Network. The proposed approach yielded better accuracy for five different datasets compared to the state of the art.
Forecasting the load of electrical power systems in mid and long term horizons A review

Load forecasting has always been an important part in the planning and operation of electric utilities, i.e. both transmission and distribution companies. With technological advancement, change in economic condition and many other factors (to be discussed in this work) load forecasting is becoming more important. The forecast affects as well as gets affected because of the load impacting factors and actions taken in different time horizons. However, due to its stochastic and uncertainty characteristics, it has been one challenging problem for electrical utilities to accurately forecast future load demand. This study aims at reviewing the different load forecasting techniques developed for the mid and long term horizons of electrical power systems. Since there has never been an explicit literature study of the various forecasting techniques for mid and long term horizons, this study reviews techniques for each of the forecasting horizons, citing various methodologies developed so far supported by published literature. This study is concluded with discussion on future research directions.
Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or 1

We introduce a method to train Binarized Neural Networks (BNNs) neural networks with binary weights and activations at run time. At training time the binary weights and activations are used for computing the parameters gradients. During the forward pass, BNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit wise operations, which is expected to substantially improve power efficiency. To validate the effectiveness of BNNs we conduct two sets of experiments on the Torch7 and Theano frameworks. On both, BNNs achieved nearly state of the art results over the MNIST, CIFAR 10 and SVHN datasets. Last but not least, we wrote a binary matrix multiplication GPU kernel with which it is possible to run our MNIST BNN 7 times faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy. The code for training and running our BNNs is available on line.
Better safe than sorry: Risky function exploitation through safe optimization
Venue: CogSci

Exploration exploitation of functions, that is learning and optimizing a mapping between inputs and expected outputs, is ubiquitous to many real world situations. These situations sometimes require us to avoid certain outcomes at all cost, for example because they are poisonous, harmful, or otherwise dangerous. We test participants' behavior in scenarios in which they have to find the optimum of a function while at the same time avoid outputs below a certain threshold. In two experiments, we find that Safe Optimization, a Gaussian Process based exploration exploitation algorithm, describes participants' behavior well and that participants seem to care firstly whether a point is safe and then try to pick the optimal point from all such safe points. This means that their trade off between exploration and exploitation can be seen as an intelligent, approximate, and homeostasis driven strategy.
A Recommender System Model based on Commodity Purchase Cycle Classification
Venue: MobiMedia

Recommender systems have been widely used in e commerce platforms, such as Amazon and Taobao. Among the available recommendation algorithms, Item Collaborative Filtering (ItemCF) Algorithm and Content Filtering Algorithm have gained wide adoption because of various strengths. For example, hidden interests can be digged so as to get fresh recommendations, and highly individual recommendations can be made. Despite their strengths and wide adoption, there are still some weaknesses associated with them. One representative weakness is the existence of duplicated, and outdated recommendations due to the lack of purchasing cycles, e.g. weekly or seasonal, of goods. We name such cycles Commodity Purchase Cycle (CPC) and propose a new recommendation algorithm based on CPC in this paper. We leverage CPC attributes to modify the collaborative filtering output rating matrix acquired by the ItemCF Algorithm, and take into consideration both user behaviors and commodity characteristics to make timely recommendations. We utilize a realistic dataset from Taobao to verify the performance of the proposed algorithm. Experimental results demonstrate good performance of CPC algorithm. Specifically, from the perspective of Root Mean Square Error (RMSE) the CPC Algorithm promotes the recommendation accuracy by 15% 20% compared with the state of the art ItemCF Algorithm.
Enhanced oxygen barrier and water resistance properties of poly(vinyl alcohol) blended with poly(acrylic acid) for packaging applications

To enhance the oxygen barrier and water resistance properties of poly(vinyl alcohol) (PVA) and expand its food packaging applicability, five crosslinked poly(vinyl alcohol)/poly(acrylic acid) (PVA/PAA) blend films were prepared via esterification reactions between hydroxyl groups in PVA and carboxylic acid groups in PAA. The physical characteristics of the blends, including the thermal, barrier, mechanical and optical properties, were investigated as a function of PAA ratio. With increasing PAA content, the crosslinking density was significantly increased, resulting in changes in the chemical structure, morphology and crystallinity of the films. The oxygen transmission rate of pure PVA decreased from 5.91 to 1.59 cc m 1 day 1 with increasing PAA ratio. The water resistance, too, increased remarkably. All the blend films showed good optical transparency. The physical properties of the blend films were strongly correlated with the chemical structure and morphology changes, which varied with the PAA content.
Effects of semi floating ring bearing outer clearance on the subsynchronous oscillation of turbocharger rotor

Semi floating ring bearing(SFRB) is developed to control the vibration of turbocharger rotor. The outer clearance of SFRB affects the magnitude and frequency of nonlinear whirl motion, which is significant for the design of turbocharger. In order to explore the effects of outer clearance, a transient finite element analysis program for rotor and oil film bearing is built and validated by a published experimental case. The nonlinear dynamic behaviors of rotor SFRB system are simulated. According to the simulation results, two representative subsynchronous oscillations excited by the two bearings respectively are discovered. As the outer clearance of SFRB increases from 24 mm to 60 mm, the low frequency subsynchronous oscillation experiences three steps, including a strong start, a gradual recession and a combination with the other one. At the same time, the high frequency subsynchronous oscillation starts to appear gradually, then strengthens, and finally combines. If gravity and unbalance are neglected, the combination will start starts from high rotor speed and extents to low rotor speed, just like a "zipper" It is found from the quantitative analysis that when the outer clearance increases, the vibration amplitude experiences large value firstly, then reduction, and suddenly increasing after combination. A useful design principle of SFRB outer clearance for minimum vibration amplitude is proposed: the outer clearance value should be chosen to keep the frequency of two subsynchronous oscillations clearly separated and their amplitudes close.
Worker Movement as a Union Issue: An Examination of Collective Bargaining Agreements in the Construction Sector in Alberta, Canada

The fluctuating expansion of oil sands development in northern Alberta, Canada has led to employers hiring a large number of mobile workers. The working conditions for some of these mobile workers are modulated in part by unions through their role in negotiating of collective bargaining agreements. Using a social reproductive framework, this study has two main findings: through collective agreements mobile workers are treated as a distinct category of worker, and there is a simultaneous expansion of workplace rules and regulations alongside a divide of the workplace from the home. The resulting expansion of the union regulated space in contrast to the divide of workplace from the home challenges union revitalization efforts, while also reaffirming traditional gendered experiences of mobility.