nils-herrmann/clean_scirepeval huggingface dataset:
Course signals at Purdue: using learning analytics to increase student success

In this paper, an early intervention solution for collegiate faculty called Course Signals is discussed. Course Signals was developed to allow instructors the opportunity to employ the power of learner analytics to provide real-time feedback to a student. Course Signals relies not only on grades to predict students' performance, but also demographic characteristics, past academic history, and students' effort as measured by interaction with Blackboard Vista, Purdue's learning management system. The outcome is delivered to the students via a personalized email from the faculty member to each student, as well as a specific color on a stoplight -- traffic signal -- to indicate how each student is doing. The system itself is explained in detail, along with retention and performance outcomes realized since its implementation. In addition, faculty and student perceptions will be shared.
The State-of-the-Art in Twitter Sentiment Analysis: A Review and Benchmark Evaluation

Twitter has emerged as a major social media platform and generated great interest from sentiment analysis researchers. Despite this attention, state-of-the-art Twitter sentiment analysis approaches perform relatively poorly with reported classification accuracies often below 70%, adversely impacting applications of the derived sentiment information. In this research, we investigate the unique challenges presented by Twitter sentiment analysis and review the literature to determine how the devised approaches have addressed these challenges. To assess the state-of-the-art in Twitter sentiment analysis, we conduct a benchmark evaluation of 28 top academic and commercial systems in tweet sentiment classification across five distinctive data sets. We perform an error analysis to uncover the causes of commonly occurring classification errors. To further the evaluation, we apply select systems in an event detection case study. Finally, we summarize the key trends and takeaways from the review and benchmark evaluation and provide suggestions to guide the design of the next generation of approaches.
Tapped-inductor buck converter for high-step-down DC-DC conversion

The narrow duty cycle in the buck converter limits its application for high-step-down dc-dc conversion. With a simple structure, the tapped-inductor buck converter shows promise for extending the duty cycle. However, the leakage inductance causes a huge turn-off voltage spike across the top switch. Also, the gate drive for the top switch is not simple due to its floating source connection. This paper solves all these problems by modifying the tapped-inductor structure. A simple lossless clamp circuit can effectively clamp the switch turn-off voltage spike and totally recover the leakage energy. Experimental results for 12V-to-1.5V and 48V-to-6V dc-dc conversions show significant improvements in efficiency.
Hilbert Space Embeddings of POMDPs

A nonparametric approach for policy learning for POMDPs is proposed. The approach represents distributions over the states, observations, and actions as embeddings in feature spaces, which are reproducing kernel Hilbert spaces. Distributions over states given the observations are obtained by applying the kernel Bayes’ rule to these distribution embeddings. Policies and value functions are defined on the feature space over states, which leads to a feature space expression for the Bellman equation. Value iteration may then be used to estimate the optimal value function and associated policy. Experimental results confirm that the correct policy is learned using the feature space representation.
Improving Comprehension of Measurements Using Concrete Re-expression Strategies

It can be difficult to understand physical measurements (e.g., 28 lb, 600 gallons) that appear in news stories, data reports, and other documents. We develop tools that automatically re-express unfamiliar measurements using the measurements of familiar objects. Our work makes three contributions: (1) we identify effectiveness criteria for objects used in concrete measurement re-expressions; (2) we operationalize these criteria in a scalable method for mining a large dataset of concrete familiar objects with their physical dimensions from Amazon and Wikipedia; and (3) we develop automated concrete re-expression tools that implement three common re-expression strategies (adding familiar context, reunitization and proportional analogy) as energy minimization algorithms. Crowdsourced evaluations of our tools indicate that people find news articles with re-expressions more helpful and re- expressions help them to better estimate new measurements.
Cognitive Control Deficits in Schizophrenia: Mechanisms and Meaning

Although schizophrenia is an illness that has been historically characterized by the presence of positive symptomatology, decades of research highlight the importance of cognitive deficits in this disorder. This review proposes that the theoretical model of cognitive control, which is based on contemporary cognitive neuroscience, provides a unifying theory for the cognitive and neural abnormalities underlying higher cognitive dysfunction in schizophrenia. To support this model, we outline converging evidence from multiple modalities (eg, structural and functional neuroimaging, pharmacological data, and animal models) and samples (eg, clinical high risk, genetic high risk, first episode, and chronic subjects) to emphasize how dysfunction in cognitive control mechanisms supported by the prefrontal cortex contribute to the pathophysiology of higher cognitive deficits in schizophrenia. Our model provides a theoretical link between cellular abnormalities (eg, reductions in dentritic spines, interneuronal dysfunction), functional disturbances in local circuit function (eg, gamma abnormalities), altered inter-regional cortical connectivity, a range of higher cognitive deficits, and symptom presentation (eg, disorganization) in the disorder. Finally, we discuss recent advances in the neuropharmacology of cognition and how they can inform a targeted approach to the development of effective therapies for this disabling aspect of schizophrenia.
Focus plus context screens: combining display technology with visualization techniques

Computer users working with large visual documents, such as large layouts, blueprints, or maps perform tasks that require them to simultaneously access overview information while working on details. To avoid the need for zooming, users currently have to choose between using a sufficiently large screen or applying appropriate visualization techniques. Currently available hi-res "wall-size" screens, however, are cost-intensive, space-intensive, or both. Visualization techniques allow the user to more efficiently use the given screen space, but in exchange they either require the user to switch between multiple views or they introduce distortion.In this paper, we present a novel approach to simultaneously display focus and context information. Focus plus context screens consist of a hi-res display and a larger low-res display. Image content is displayed such that the scaling of the display content is preserved, while its resolution may vary according to which display region it is displayed in. Focus plus context screens are applicable to practically all tasks that currently use overviews or fisheye views, but unlike these visualization techniques, focus plus context screens provide a single, non-distorted view. We present a prototype that seamlessly integrates an LCD with a projection screen and demonstrate four applications that we have adapted so far.
FPGA vs. GPU for sparse matrix vector multiply

Sparse matrix-vector multiplication (SpMV) is a common operation in numerical linear algebra and is the computational kernel of many scientific applications. It is one of the original and perhaps most studied targets for FPGA acceleration. Despite this, GPUs, which have only recently gained both general-purpose programmability and native support for double precision floating-point arithmetic, are viewed by some as a more effective platform for SpMV and similar linear algebra computations. In this paper, we present an analysis comparing an existing GPU SpMV implementation to our own, novel FPGA implementation. In this analysis, we describe the challenges faced by any SpMV implementation, the unique approaches to these challenges taken by both FPGA and GPU implementations, and their relative performance for SpMV.
Sirolimus-eluting stents versus standard stents in patients with stenosis in a native coronary artery.

Preliminary reports of studies involving simple coronary lesions indicate that a sirolimus-eluting stent significantly reduces the risk of restenosis after percutaneous coronary revascularization.



We conducted a randomized, double-blind trial comparing a sirolimus-eluting stent with a standard stent in 1058 patients at 53 centers in the United States who had a newly diagnosed lesion in a native coronary artery. The coronary disease in these patients was complex because of the frequent presence of diabetes (in 26 percent of patients), the high percentage of patients with longer lesions (mean, 14.4 mm), and small vessels (mean, 2.80 mm). The primary end point was failure of the target vessel (a composite of death from cardiac causes, myocardial infarction, and repeated percutaneous or surgical revascularization of the target vessel) within 270 days.



The rate of failure of the target vessel was reduced from 21.0 percent with a standard stent to 8.6 percent with a sirolimus-eluting stent (P<0.001)--a reduction that was driven largely by a decrease in the frequency of the need for revascularization of the target lesion (16.6 percent in the standard-stent group vs. 4.1 percent in the sirolimus-stent group, P<0.001). The frequency of neointimal hyperplasia within the stent was also decreased in the group that received sirolimus-eluting stents, as assessed by both angiography and intravascular ultrasonography. Subgroup analyses revealed a reduction in the rates of angiographic restenosis and target-lesion revascularization in all subgroups examined.



In this randomized clinical trial involving patients with complex coronary lesions, the use of a sirolimus-eluting stent had a consistent treatment effect, reducing the rates of restenosis and associated clinical events in all subgroups analyzed.
Double Synchronous Reference Frame PLL for Power Converters Control

This paper deals with one of the most important issues in the control of grid-connected converters, which is the detection of the positive sequence fundamental component of the utility voltage. The study carried out in this paper conducts to a fast, precise, and robust positive sequence voltage detector offering a good behavior, even if unbalanced and distorted conditions are present in the grid. The proposed detector utilizes a new "double synchronous reference frame PLL" (DSRF-PLL), which completely eliminates the existing errors in conventional synchronous reference frame PLL systems (SRF-PLL) when operating under unbalanced utility voltages. In the study performed in this paper, the positive and negative sequence components of the unbalanced voltage vector are properly characterized. When this unbalanced vector is expressed on the DSRF, the analysis of the signals on the DSRF axes permits to design a decoupling network which isolates the positive and negative sequence components. This decoupling network gives rise to a new PLL structure which detects the positive sequence voltage component quickly and accurately. In this paper, conclusions of the analytical study are verified by simulation and experiment
Development of an Assessment Model for Industry 4.0: Industry 4.0-MM

The application of new technologies in the manufacturing environment is ushering a new era referred to as the 4th industrial revolution, and this digital transformation appeals to companies due to various competitive advantages it provides. Accordingly, there is a fundamental need for assisting companies in the transition to Industry 4.0 technologies/practices, and guiding them for improving their capabilities in a standardized, objective, and repeatable way. Maturity Models (MMs) aim to assist organizations by providing comprehensive guidance. Therefore, the literature is reviewed systematically with the aim of identifying existing studies related to MMs proposed in the context of Industry 4.0. Seven identified MMs are analyzed by comparing their characteristics of scope, purpose, completeness, clearness, and objectivity. It is concluded that none of them satisfies all expected criteria. In order to satisfy the need for a structured Industry 4.0 assessment/maturity model, SPICE-based Industry 4.0-MM is proposed in this study. Industry 4.0-MM has a holistic approach consisting of the assessment of process transformation, application management, data governance, asset management, and organizational alignment areas. The aim is to create a common base for performing an assessment of the establishment of Industry 4.0 technologies, and to guide companies towards achieving a higher maturity stage in order to maximize the economic benefits of Industry 4.0. Hence, Industry 4.0-MM provides standardization in continuous benchmarking and improvement of businesses in the manufacturing industry.
Pattern Reconfigurable Antenna Based on Morphing Bistable Composite Laminates

In this paper, a novel pattern reconfigurable antenna based on morphing bistable composite laminates is presented. The bistable asymmetric glass-fiber reinforced polymer (GFRP) composite laminates have two stable configurations with curvatures of opposite signs. The antenna pattern is reconfigured by transforming the configuration of the bistable GFRP laminate which acts as the substrate of the antenna. The coplanar waveguide transmission lines feeding technique is used for the microstrip quasi-Yagi antenna. A prototype of the proposed antenna is fabricated using a semi-automatic screen printer and an autoclave. The transformation between the two stable states of the proposed antenna using Ni/Ti shape memory alloy springs is investigated experimentally. The out-of-plane displacements, reflection coefficients and radiation patterns for the two stable configurations of the antenna are measured, which agree well with the simulated results. The main beam direction is 89° and 59° for the two stable configurations, respectively. In addition, the influences of various bending radii on the radiation patterns are investigated to gain a thorough understanding of the reconfigurable mechanism of the proposed antenna. Finally, a two-element array of such an antenna is presented and measured. The proposed antenna provides a potential application in multifunctional, conformal, morphing, and integrated structures.
Frequent itemset mining of uncertain data streams using the damped window model

With advances in technology, large amounts of streaming data can be generated continuously by sensors in applications like environment surveillance. Due to the inherited limitation of sensors, these continuous data can be uncertain. This calls for stream mining of uncertain data. In recent years, tree-based algorithms have been proposed to use the sliding window model for mining frequent itemsets from streams of uncertain data. Besides the sliding window model, there are other window models for processing data streams. In this paper, we propose tree-based algorithms that use the damped window model to mine frequent itemsets from streams of uncertain data.
Drop-on-Demand Solder Droplet Jetting System for Fabricating Microstructure

The novel drop-on-demand (DOD) solder droplet jetting system, which can be characterized as the non-heat affection of the piezoelectric actuator, the control mechanism of the piston head-to-nozzle distance, and disposable nozzle parts, is devised. This system consists of the piezoelectric actuator, insulator, piston head-to-nozzle distance control apparatus, pressure control unit, and furnace. For the modulation of the droplet size, the distance from piston head-to-nozzle exit can be controlled by using the piston head-to-nozzle distance control apparatus. The working temperature is improved by locating the piezoelectric actuator outside of the furnace and by inserting the insulation block between the print head and the actuator. From a practical point of view, the DOD solder droplet jetting system has a simple structure for easily interchangeable nozzle parts. We derived the equation of the ejected droplet volume as a function of design parameters and operating conditions. The prototype of the DOD solder droplet jetting system was fabricated and the performance was verified. The diameter, volume and velocity of the ejected solder droplet are around 65-150 mum, 140 pl-1.8 nl, and 2.8-4 m/s, respectively. We also experimented with the effect of varying the chamber pressure, piston head-to-nozzle distance, and operating frequency on the ejected droplet diameter and velocity. The high aspect ratio vertical columns and inclined columns were fabricated by using the DOD solder droplet jetting system.
A Case Study on Barriers and Enhancements of the PET Bottle-to-Bottle Recycling Systems in Germany and Sweden

The demand of beverages in PET bottles is constantly increasing. In this context, environmental, technological and regulatory aspects set a stronger focus on recycling. Generally, the reuse of recycled material from post-consumer PET bottles in bottle-to-bottle applications is seen as least environmentally harmful. However, closedloop systems are not widely implemented in Europe. Previous research mainly focuses on open-loop recycling systems and generally lacks discussion about the current German and Swedish systems and their challenges. Furthermore, previous studies lack theoretical and practical enhancements for bottle-to-bottle recycling from a managerial perspective.  The purpose of this study is to compare the PET bottle recycling systems in Germany and Sweden, analyse the main barriers and develop enhancements for closedloop systems.  This qualitative study employs a case study strategy about the two cases of Germany and Sweden. In total, 14 semi-structured interviews are conducted with respondents from different industry sectors within the PET bottle recycling systems. The empirical data is categorised and then analysed by pattern matching with the developed theoretical framework.  Due to the theoretical and practical commitment to closed-loop recycling, the Swedish PET bottle recycling system outperforms the Germany system. In Germany, bottle-to-bottle recycling is currently performed on a smaller scale without a unified system. The main barriers for bottle-to-bottle recycling are distinguished into (1) quality and material factors, (2) regulatory and legal factors, (3) economic and market factors and (4) factors influenced by consumers. The enhancements for the systems are (1) quality and material factors, (2) regulatory and legal factors, (3) recollection factors and (4) expanding factors. Lastly, the authors provide further recommendations, which are (1) a recycling content symbol on bottle labels, (2) a council for bottle quality in Germany, (3) a quality seal for the holistic systems, (4) a reduction of transportation in Sweden and (5) an increase of consumer awareness on PET bottle consumption.
Radial Sets: Interactive Visual Analysis of Large Overlapping Sets

In many applications, data tables contain multi-valued attributes that often store the memberships of the table entities to multiple sets such as which languages a person masters, which skills an applicant documents, or which features a product comes with. With a growing number of entities, the resulting element-set membership matrix becomes very rich of information about how these sets overlap. Many analysis tasks targeted at set-typed data are concerned with these overlaps as salient features of such data. This paper presents Radial Sets, a novel visual technique to analyze set memberships for a large number of elements. Our technique uses frequency-based representations to enable quickly finding and analyzing different kinds of overlaps between the sets, and relating these overlaps to other attributes of the table entities. Furthermore, it enables various interactions to select elements of interest, find out if they are over-represented in specific sets or overlaps, and if they exhibit a different distribution for a specific attribute compared to the rest of the elements. These interactions allow formulating highly-expressive visual queries on the elements in terms of their set memberships and attribute values. As we demonstrate via two usage scenarios, Radial Sets enable revealing and analyzing a multitude of overlapping patterns between large sets, beyond the limits of state-of-the-art techniques.
Template-based question answering over RDF data

As an increasing amount of RDF data is published as Linked Data, intuitive ways of accessing this data become more and more important. Question answering approaches have been proposed as a good compromise between intuitiveness and expressivity. Most question answering systems translate questions into triples which are matched against the RDF data to retrieve an answer, typically relying on some similarity metric. However, in many cases, triples do not represent a faithful representation of the semantic structure of the natural language question, with the result that more expressive queries can not be answered. To circumvent this problem, we present a novel approach that relies on a parse of the question to produce a SPARQL template that directly mirrors the internal structure of the question. This template is then instantiated using statistical entity identification and predicate detection. We show that this approach is competitive and discuss cases of questions that can be answered with our approach but not with competing approaches.
Modeling Trajectories with Recurrent Neural Networks

Modeling trajectory data is a building block for many smart-mobility initiatives. Existing approaches apply shallow models such as Markov chain and inverse reinforcement learning to model trajectories, which cannot capture the long-term dependencies. On the other hand, deep models such as Recurrent Neural Network (RNN) have demonstrated their strength of modeling variable length sequences. However, directly adopting RNN to model trajectories is not appropriate because of the unique topological constraints faced by trajectories. Motivated by these findings, we design two RNNbased models which can make full advantage of the strength of RNN to capture variable length sequence and meanwhile to address the constraints of topological structure on trajectory modeling. Our experimental study based on real taxi trajectory datasets shows that both of our approaches largely outperform the existing approaches.