nils-herrmann/clean_scirepeval huggingface dataset:
A Machine Learning Approach for Assessment and Prediction of Teamwork Effectiveness in Software Engineering Education

One of the challenges in effective software engineering (SE) education is the lack of objective assessment methods of how well student teams learn the critically needed teamwork practices, defined as the ability: (i) to learn and effectively apply SE processes in a teamwork setting, and (ii) to work as a team to develop satisfactory software (SW) products. In addition, there are no effective methods for predicting learning effectiveness in order to enable early intervention in the classroom. Most of the current approaches to assess achievement of SE teamwork skills rely solely on qualitative and subjective data taken as surveys at the end of the class and analyzed only with very rudimentary data analysis. In this paper we present a novel approach to address the assessment and prediction of student learning of teamwork effectiveness in software engineering education based on: a) extracting only objective and quantitative student team activity data during their team class project; b) pairing these data with related independent observations and grading of student team effectiveness in SE process and SE product components in order to create “training database”; and c) applying a machine learning (ML) approach, namely random forest classification (RF), to the above training database in order to create ML models, ranked factors and rules that can both explain (e.g. assess) as well as provide prediction of the student teamwork effectiveness. These student team activity data are being collected in joint and already established (since 2006) SE classes at San Francisco State University (SFSU), Florida Atlantic University (FAU) and Fulda University, Germany (Fulda), from approximately 80 students each year, working in about 15 teams, both local and global (with students from multiple schools). ; software engineering teamwork; software engineering education; assessment; machine learning.
Architectural Style Classification of Building Facade Windows

Building facade classification by architectural styles allows categorization of large databases of building images into semantic categories belonging to certain historic periods, regions and cultural influences. Image databases sorted by architectural styles permit effective and fast image search for the purposes of content-based image retrieval, 3D reconstruction, 3D city-modeling, virtual tourism and indexing of cultural heritage buildings. Building facade classification is viewed as a task of classifying separate architectural structural elements, like windows, domes, towers, columns, etc, as every architectural style applies certain rules and characteristic forms for the design and construction of the structural parts mentioned. In the context of building facade architectural style classification the current paper objective is to classify the architectural style of facade windows. Typical windows belonging to Romanesque, Gothic and Renaissance/Baroque European main architectural periods are classified. The approach is based on clustering and learning of local features, applying intelligence that architects use to classify windows of the mentioned architectural styles in the training stage.
Using Big Data to Estimate Consumer Surplus : The Case of Uber

Estimating consumer surplus is challenging because it requires identification of the entire demand curve. We rely on Uber’s “surge” pricing algorithm and the richness of its individual level data to first estimate demand elasticities at several points along the demand curve. We then use these elasticity estimates to estimate consumer surplus. Using almost 50 million individuallevel observations and a regression discontinuity design, we estimate that in 2015 the UberX service generated about $2.9 billion in consumer surplus in the four U.S. cities included in our analysis. For each dollar spent by consumers, about $1.60 of consumer surplus is generated. Back-of-the-envelope calculations suggest that the overall consumer surplus generated by the UberX service in the United States in 2015 was $6.8 billion.
Conditions and circumstances predisposing to death from positional asphyxia in adults.

Positional asphyxia refers to a situation where there is compromise of respiration because of splinting of the chest and/or diaphragm preventing normal respiratory excursion, or occlusion of the upper airway due to abnormal positioning of the body. Examination of autopsy files at Forensic Science SA revealed instances where positional asphyxia resulted from inadvertent positioning that compromised respiration due to intoxication, multiple sclerosis, epilepsy, Parkinson disease, Steele-Richardson-Olszewski syndrome, Lafora disease and quadriplegia. While the manner of death was accidental in most cases, in one instance suicide could not be ruled out. We would not exclude the possibility of individuals with significant cardiac disease succumbing to positional asphyxia, as cardiac disease may be either unrelated to the terminal episode or, alternatively, may result in collapse predisposing to positional asphyxia. Victims of positional asphyxia do not extricate themselves from dangerous situations due to impairment of cognitive responses and coordination resulting from intoxication, sedation, neurological diseases, loss of consciousness, physical impairment or physical restraints.
LiFi: Line-Of-Sight identification with WiFi

Wireless LANs, especially WiFi, have been pervasively deployed and have fostered myriad wireless communication services and ubiquitous computing applications. A primary concern in designing each scenario-tailored application is to combat harsh indoor propagation environments, particularly Non-Line-Of-Sight (NLOS) propagation. The ability to distinguish Line-Of-Sight (LOS) path from NLOS paths acts as a key enabler for adaptive communication, cognitive radios, robust localization, etc. Enabling such capability on commodity WiFi infrastructure, however, is prohibitive due to the coarse multipath resolution with mere MAC layer RSSI. In this work, we dive into the PHY layer and strive to eliminate irrelevant noise and NLOS paths with long delays from the multipath channel responses. To further break away from the intrinsic bandwidth limit of WiFi, we extend to the spatial domain and harness natural mobility to magnify the randomness of NLOS paths while retaining the deterministic nature of the LOS component. We prototype LiFi, a statistical LOS identification scheme for commodity WiFi infrastructure and evaluate it in typical indoor environments covering an area of 1500 m2. Experimental results demonstrate an overall LOS identification rate of 90.4% with a false alarm rate of 9.3%.
Formal certification of a compiler back-end or: programming a compiler with a proof assistant

This paper reports on the development and formal certification (proof of semantic preservation) of a compiler from Cminor (a C-like imperative language) to PowerPC assembly code, using the Coq proof assistant both for programming the compiler and for proving its correctness. Such a certified compiler is useful in the context of formal methods applied to the certification of critical software: the certification of the compiler guarantees that the safety properties proved on the source code hold for the executable compiled code as well.
Comparison of cadaveric and isomorphic three-dimensional printed models in temporal bone education.

Current three-dimensional (3D) printed simulations are complicated by insufficient void spaces and inconsistent density. We describe a novel simulation with focus on internal anatomic fidelity and evaluate against template/identical cadaveric education.



Research ethics board-approved prospective cohort study.



Generation of a 3D printed temporal bone was performed using a proprietary algorithm that deconstructs the digital model into slices prior to printing. This supplemental process facilitates removal of residual material from air-containing spaces and permits requisite infiltrative access to the all regions of the model. Ten otolaryngology trainees dissected a cadaveric temporal bone (CTB) followed by a matched/isomorphic 3D printed bone model (PBM), based on derivative micro-computed tomography data. Participants rated 1) physical characteristics, 2) specific anatomic constructs, 3) usefulness in skill development, and 4) perceived educational value. The survey instrument employed a seven-point Likert scale.



Trainees felt physical characteristics of the PBM were quite similar to CTB, with highly ranked cortical (5.5 ± 1.5) and trabecular (5.2 ± 1.3) bone drill quality. The overall model was considered comparable to CTB (5.9 ± 0.74), with respectable air cell reproduction (6.1 ± 1.1). Internal constructs were rated as satisfactory (range, 4.9-6.2). The simulation was considered a beneficial training tool for all types of mastoidectomy (range, 5.9-6.6), posterior tympanotomy (6.5 ± 0.71), and skull base approaches (range, 6-6.5). Participants believed the model to be an effective training instrument (6.7 ± 0.68), which should be incorporated into the temporal bone lab (7.0 ± 0.0). The PBM was thought to improve confidence (6.7 ± 0.68) and operative performance (6.7 ± 0.48).



Study participants found the PBM to be an effective platform that compared favorably to CTB. The model was considered a valuable adjunctive training tool with both realistic mechanical and visual character.
Bandit Processes and Dynamic Allocation Indices

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Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations

In this paper, we describe the design of Neurogrid, a neuromorphic system for simulating large-scale neural models in real time. Neuromorphic systems realize the function of biological neural systems by emulating their structure. Designers of such systems face three major design choices: 1) whether to emulate the four neural elements-axonal arbor, synapse, dendritic tree, and soma-with dedicated or shared electronic circuits; 2) whether to implement these electronic circuits in an analog or digital manner; and 3) whether to interconnect arrays of these silicon neurons with a mesh or a tree network. The choices we made were: 1) we emulated all neural elements except the soma with shared electronic circuits; this choice maximized the number of synaptic connections; 2) we realized all electronic circuits except those for axonal arbors in an analog manner; this choice maximized energy efficiency; and 3) we interconnected neural arrays in a tree network; this choice maximized throughput. These three choices made it possible to simulate a million neurons with billions of synaptic connections in real time-for the first time-using 16 Neurocores integrated on a board that consumes three watts.
Simulating makeup through physics-based manipulation of intrinsic image layers

We present a method for simulating makeup in a face image. To generate realistic results without detailed geometric and reflectance measurements of the user, we propose to separate the image into intrinsic image layers and alter them according to proposed adaptations of physically-based reflectance models. Through this layer manipulation, the measured properties of cosmetic products are applied while preserving the appearance characteristics and lighting conditions of the target face. This approach is demonstrated on various forms of cosmetics including foundation, blush, lipstick, and eye shadow. Experimental results exhibit a close approximation to ground truth images, without artifacts such as transferred personal features and lighting effects that degrade the results of image-based makeup transfer methods.
Predictive positioning and quality of service ridesharing for campus mobility on demand systems

Autonomous Mobility On Demand (MOD) systems can utilize fleet management strategies in order to provide a high customer quality of service (QoS). Previous works on autonomous MOD systems have developed methods for rebalancing single capacity vehicles, where QoS is maintained through large fleet sizing. This work focuses on MOD systems utilizing a small number of vehicles, such as those found on a campus, where additional vehicles cannot be introduced as demand for rides increases. A predictive positioning method is presented for improving customer QoS by identifying key locations to position the fleet in order to minimize expected customer wait time. Ridesharing is introduced as a means for improving customer QoS as arrival rates increase. However, with ridesharing perceived QoS is dependent on an often unknown customer preference. To address this challenge, a customer ratings model, which learns customer preference from a 5-star rating, is developed and incorporated directly into a ridesharing algorithm. The predictive positioning and ridesharing methods are applied to simulation of a real-world campus MOD system. A combined predictive positioning and ridesharing approach is shown to reduce customer service times by up to 29%. and the customer ratings model is shown to provide the best overall MOD fleet management performance over a range of customer preferences.
The Well-Worn Route and the Path Less Traveled Distinct Neural Bases of Route Following and Wayfinding in Humans

Finding one's way in a large-scale environment may engage different cognitive processes than following a familiar route. The neural bases of these processes were investigated using functional MRI (fMRI). Subjects found their way in one virtual-reality town and followed a well-learned route in another. In a control condition, subjects followed a visible trail. Within subjects, accurate wayfinding activated the right posterior hippocampus. Between-subjects correlations with performance showed that good navigators (i.e., accurate wayfinders) activated the anterior hippocampus during wayfinding and head of caudate during route following. These results coincide with neurophysiological evidence for distinct response (caudate) and place (hippocampal) representations supporting navigation. We argue that the type of representation used influences both performance and concomitant fMRI activation patterns.
A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation.

A meta-analysis of 128 studies examined the effects of extrinsic rewards on intrinsic motivation. As predicted, engagement-contingent, completion-contingent, and performance-contingent rewards significantly undermined free-choice intrinsic motivation (d = -0.40, -0.36, and -0.28, respectively), as did all rewards, all tangible rewards, and all expected rewards. Engagement-contingent and completion-contingent rewards also significantly undermined self-reported interest (d = -0.15, and -0.17), as did all tangible rewards and all expected rewards. Positive feedback enhanced both free-choice behavior (d = 0.33) and self-reported interest (d = 0.31). Tangible rewards tended to be more detrimental for children than college students, and verbal rewards tended to be less enhancing for children than college students. The authors review 4 previous meta-analyses of this literature and detail how this study's methods, analyses, and results differed from the previous ones.
Predicting Sufficient Annotation Strength for Interactive Foreground Segmentation

The mode of manual annotation used in an interactive segmentation algorithm affects both its accuracy and ease-of-use. For example, bounding boxes are fast to supply, yet may be too coarse to get good results on difficult images, freehand outlines are slower to supply and more specific, yet they may be overkill for simple images. Whereas existing methods assume a fixed form of input no matter the image, we propose to predict the tradeoff between accuracy and effort. Our approach learns whether a graph cuts segmentation will succeed if initialized with a given annotation mode, based on the image's visual separability and foreground uncertainty. Using these predictions, we optimize the mode of input requested on new images a user wants segmented. Whether given a single image that should be segmented as quickly as possible, or a batch of images that must be segmented within a specified time budget, we show how to select the easiest modality that will be sufficiently strong to yield high quality segmentations. Extensive results with real users and three datasets demonstrate the impact.
Bézier Curve for Trajectory Guidance

In this paper we present two path planning algorithms based on Bézier curves for autonomous vehicles with waypoints and corridor constraints. Bézier curves have useful properties for the path generation problem. The paper describes how the algorithms apply these properties to generate the reference trajectory for vehicles to satisfy the path constraints. Both algorithms join cubic Bézier curve segments smoothly to generate the path. Additionally, we discuss the constrained optimization problem that optimizes the resulting path for user-defined cost function. The simulation shows the generation of successful routes for autonomous vehicles using these algorithms as well as control results for a simple kinematic vehicle. Extensions of these algorithms towards navigating through an unstructured environment with limited sensor range are discussed.
VSA-HD: From the enumeration analysis to the prototypical implementation

This paper presents design, implementation and performance of a new Variable Stiffness Actuator (VSA) based on Harmonic Drives (VSA-HD), which is an improvement over past work reported in , . While previous prototypes have been developed to demonstrate the effectiveness of the variable stiffness actuation principle and the possibility to develop a compact and reliable actuator, the VSA-HD has been obtained by exploring the performance of the enumeration of all VSA made out a basic components set (i.e. two prime movers, two harmonic-drive gears, and the output shaft) and all the feasible interconnections between them as presented in . Along this enumeration the VSA-HD conceptual layout has been selected as being good trade-off between mechanical complexity and overall performance. This paper discusses in depth the actuator mechanical layout, highlighting the main characteristics of the new design. A model for the actuator is introduced and validated by experimental results.
Gust loading factor — past , present and future

Wind loads on structures under the buffeting action of wind gusts have traditionally been treated by the ‘‘gust loading factor’’ (GLF) method in most major codes and standards around the world. In this scheme, the equivalent-static wind loading used for design is equal to the mean wind force multiplied by the GLF. Although the traditional GLF method ensures an accurate estimation of the displacement response, it may fall short in providing a reliable estimate of other response components. To overcome this shortcoming, a more consistent procedure for determining design loads on tall structures is proposed. This paper highlights an alternative model, in which the GLF is based on the base bending moment rather than the displacement. The expected extreme base moment is computed by multiplying the mean base moment by the proposed GLF. The base moment is then distributed to each floor in terms of the floor load in a format that is very similar to the one used to distribute the base shear in earthquake engineering practice. In addition, a simple relationship between the proposed base moment GLF and the traditional GLF is derived, which makes it convenient to employ the proposed approach while utilizing the existing background information. Numerical examples are presented to demonstrate the efficacy of the proposed procedure in light of the traditional approach. This paper also extends the new framework for the formulation of wind load effects in the acrosswind and torsional directions along the ‘‘GLF’’ format that has generally been used for the alongwind response. A 3D GLF concept is advanced, which draws upon a database of aerodynamic wind loads on typical tall buildings, a mode shape correction procedure and a more realistic formulation of the equivalent-static wind loads and their effects. A numerical example is presented to demonstrate the efficacy of the proposed procedure in light of the traditional approach. It is envisaged that the proposed formulation will be most appropriate for inclusion in codes and standards. r 2003
Joint Extraction of Entities and Relations for Opinion Recognition

We present an approach for the joint extraction of entities and relations in the context of opinion recognition and analysis. We identify two types of opinion-related entities — expressions of opinions and sources of opinions — along with the linking relation that exists between them. Inspired by Roth and Yih (2004), we employ an integer linear programming approach to solve the joint opinion recognition task, and show that global, constraint-based inference can significantly boost the performance of both relation extraction and the extraction of opinion-related entities. Performance further improves when a semantic role labeling system is incorporated. The resulting system achieves F-measures of 79 and 69 for entity and relation extraction, respectively, improving substantially over prior results in the area.
A hybrid nanomemristor/transistor logic circuit capable of self-programming.

Memristor crossbars were fabricated at 40 nm half-pitch, using nanoimprint lithography on the same substrate with Si metal-oxide-semiconductor field effect transistor (MOS FET) arrays to form fully integrated hybrid memory resistor (memristor)/transistor circuits. The digitally configured memristor crossbars were used to perform logic functions, to serve as a routing fabric for interconnecting the FETs and as the target for storing information. As an illustrative demonstration, the compound Boolean logic operation (A AND B) OR (C AND D) was performed with kilohertz frequency inputs, using resistor-based logic in a memristor crossbar with FET inverter/amplifier outputs. By routing the output signal of a logic operation back onto a target memristor inside the array, the crossbar was conditionally configured by setting the state of a nonvolatile switch. Such conditional programming illuminates the way for a variety of self-programmed logic arrays, and for electronic synaptic computing.
Defect Inspection in Low-Contrast LCD Images Using Hough Transform-Based Nonstationary Line Detection

In this paper, we propose a Hough transform-based method to identify low-contrast defects in unevenly illuminated images, and especially focus on the inspection of mura defects in liquid crystal display (LCD) panels. The proposed method works on 1-D gray-level profiles in the horizontal and vertical directions of the surface image. A point distinctly deviated from the ideal line of a profile can be identified as a defect one. A 1-D gray-level profile in the unevenly illuminated image results in a nonstationary line signal. The most commonly used technique for straight line detection in a noisy image is Hough transform (HT). The standard HT requires a sufficient number of points lie exactly on the same straight line at a given parameter resolution so that the accumulator will show a distinct peak in the parameter space. It fails to detect a line in a nonstationary signal. In the proposed HT scheme, the points that contribute to the vote do not have to lie on a line. Instead, a distance tolerance to the line sought is first given. Any point with the distance to the line falls within the tolerance will be accumulated by taking the distance as the voting weight. A fast search procedure to tighten the possible ranges of line parameters is also proposed for mura detection in LCD images.