nils-herrmann/clean_scirepeval huggingface dataset:
High Accuracy and Visibility-Consistent Dense Multiview Stereo

Since the initial comparison of Seitz et al. , the accuracy of dense multiview stereovision methods has been increasing steadily. A number of limitations, however, make most of these methods not suitable to outdoor scenes taken under uncontrolled imaging conditions. The present work consists of a complete dense multiview stereo pipeline which circumvents these limitations, being able to handle large-scale scenes without sacrificing accuracy. Highly detailed reconstructions are produced within very reasonable time thanks to two key stages in our pipeline: a minimum s-t cut optimization over an adaptive domain that robustly and efficiently filters a quasidense point cloud from outliers and reconstructs an initial surface by integrating visibility constraints, followed by a mesh-based variational refinement that captures small details, smartly handling photo-consistency, regularization, and adaptive resolution. The pipeline has been tested over a wide range of scenes: from classic compact objects taken in a laboratory setting, to outdoor architectural scenes, landscapes, and cultural heritage sites. The accuracy of its reconstructions has also been measured on the dense multiview benchmark proposed by Strecha et al. , showing the results to compare more than favorably with the current state-of-the-art methods.
Intrinsic motivation and the effects of self-consciousness , self-awareness , and ego-involvement : An investigation of internally controlling styles

This study explored the relationships among dispositional self-consciousness, situationally induced-states of self-awareness, ego-mvolvement, and intrinsic motivation Cognitive evaluation theory, as applied to both the interpersonal and intrapersonal spheres, was used as the basis for making predictions about the effects of various types of self-focus Public selfconsciousness, social anxiety, video surveillance and mirror manipulations of self-awareness, and induced ego-involvement were predicted and found to have negative effects on intrinsic motivation since all were hypothesized to involve controlling forms of regulation In contrast, dispositional pnvate self-consciousness and a no-self-focus condition were both found to be unrelated to intrinsic motivation The relationship among these constructs and manipulations was discussed in the context of both Carver and Scheier's (1981) control theory and Deci and Ryan's (1985) motivation theory Recent theory and research on self-awareness, stimulated bv the initial findings of Duval and Wicklund (1972), has suggested that qualitatively distinct styles of attention and consciousness can be involved in the ongoing process of self-regulation (Carver & Scheier, 1981) In particular, Fenigstein, Scheier, and Buss (1975) have distinguished between private self-consciousness and pubhc selfconsciousness as two independent, but not necessarily exclusive, types of attentional focus with important behavioral, cognitive, and affective implications for regulatory processes Pnvate self-consciousness refers to the tendency to be aware of one's thoughts. This research was supported by Research Grant BSN-8018628 from the National Science Foundation The authors are grateful to the following persons who helped this project to reach fruition James Connell Edward Deci, Paul Tero, Shirlev Tracey We are also grateful for the experimental assistance of Margot Cohen Scott Cohen, Loren Feldman, and Darrell Mazlish Thanks also to Eileen Plant and Miriam Gale Requests for reprints should be sent to Richard M Ryan, Department of Psychology, Uniyersity of Rochester, Rochester, NY 14627 Journal of Personality 53 3, September 1985
Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders

Lesion detection in brain Magnetic Resonance Images (MRI) remains a challenging task. State-of-the-art approaches are mostly based on supervised learning making use of large annotated datasets. Human beings, on the other hand, even nonexperts, can detect most abnormal lesions after seeing a handful of healthy brain images. Replicating this capability of using prior information on the appearance of healthy brain structure to detect lesions can help computers achieve human level abnormality detection, specifically reducing the need for numerous labeled examples and bettering generalization of previously unseen lesions. To this end, we study detection of lesion regions in an unsupervised manner by learning data distribution of brain MRI of healthy subjects using auto-encoder based methods. We hypothesize that one of the main limitations of the current models is the lack of consistency in latent representation. We propose a simple yet effective constraint that helps mapping of an image bearing lesion close to its corresponding healthy image in the latent space. We use the Human Connectome Project dataset to learn distribution of healthy-appearing brain MRI and report improved detection, in terms of AUC, of the lesions in the BRATS challenge dataset.
Fast and Accurate Time-Series Clustering

The proliferation and ubiquity of temporal data across many disciplines has generated substantial interest in the analysis and mining of time series. Clustering is one of the most popular data-mining methods, not only due to its exploratory power but also because it is often a preprocessing step or subroutine for other techniques. In this article, we present k-Shape and k-MultiShapes (k-MS), two novel algorithms for time-series clustering. k-Shape and k-MS rely on a scalable iterative refinement procedure. As their distance measure, k-Shape and k-MS use shape-based distance (SBD), a normalized version of the cross-correlation measure, to consider the shapes of time series while comparing them. Based on the properties of SBD, we develop two new methods, namely ShapeExtraction (SE) and MultiShapesExtraction (MSE), to compute cluster centroids that are used in every iteration to update the assignment of time series to clusters. k-Shape relies on SE to compute a single centroid per cluster based on all time series in each cluster. In contrast, k-MS relies on MSE to compute multiple centroids per cluster to account for the proximity and spatial distribution of time series in each cluster. To demonstrate the robustness of SBD, k-Shape, and k-MS, we perform an extensive experimental evaluation on 85 datasets against state-of-the-art distance measures and clustering methods for time series using rigorous statistical analysis. SBD, our efficient and parameter-free distance measure, achieves similar accuracy to Dynamic Time Warping (DTW), a highly accurate but computationally expensive distance measure that requires parameter tuning. For clustering, we compare k-Shape and k-MS against scalable and non-scalable partitional, hierarchical, spectral, density-based, and shapelet-based methods, with combinations of the most competitive distance measures. k-Shape outperforms all scalable methods in terms of accuracy. Furthermore, k-Shape also outperforms all non-scalable approaches, with one exception, namely k-medoids with DTW, which achieves similar accuracy. However, unlike k-Shape, this approach requires tuning of its distance measure and is significantly slower than k-Shape. k-MS performs similarly to k-Shape in comparison to rival methods, but k-MS is significantly more accurate than k-Shape. Beyond clustering, we demonstrate the effectiveness of k-Shape to reduce the search space of one-nearest-neighbor classifiers for time series. Overall, SBD, k-Shape, and k-MS emerge as domain-independent, highly accurate, and efficient methods for time-series comparison and clustering with broad applications.
RotBoost: A technique for combining Rotation Forest and AdaBoost

This paper presents a novel ensemble classifier generation technique RotBoost, which is constructed by combining Rotation Forest and AdaBoost. The experiments conducted with 36 real-world data sets available from the UCI repository, among which a classification tree is adopted as the base learning algorithm, demonstrate that RotBoost can generate ensemble classifiers with significantly lower prediction error than either Rotation Forest or AdaBoost more often than the reverse. Meanwhile, RotBoost is found to perform much better than Bagging and MultiBoost. Through employing the bias and variance decompositions of error to gain more insight of the considered classification methods, RotBoost is seen to simultaneously reduce the bias and variance terms of a single tree and the decrement achieved by it is much greater than that done by the other ensemble methods, which leads RotBoost to perform best among the considered classification procedures. Furthermore, RotBoost has a potential advantage over AdaBoost of suiting parallel execution.
Advanced procedural modeling of architecture

We present the novel grammar language CGA++ for the procedural modeling of architecture. While existing grammar-based approaches can produce stunning results, they are limited in what modeling scenarios can be realized. In particular, many context-sensitive tasks are precluded, not least because within the rules specifying how one shape is refined, the necessary knowledge about other shapes is not available. Transcending such limitations, CGA++ significantly raises the expressiveness and offers a generic and integrated solution for many advanced procedural modeling problems. Pivotally, CGA++ grants first-class citizenship to shapes, enabling, within a grammar, directly accessing shapes and shape trees, operations on multiple shapes, rewriting shape (sub)trees, and spawning new trees (e.g., to explore multiple alternatives). The new linguistic device of events allows coordination across multiple shapes, featuring powerful dynamic grouping and synchronization. Various examples illustrate CGA++, demonstrating solutions to previously infeasible modeling challenges.
Using recurrent neural network models for early detection of heart failure onset

We explored whether use of deep learning to model temporal relations among events in electronic health records (EHRs) would improve model performance in predicting initial diagnosis of heart failure (HF) compared to conventional methods that ignore temporality.



Data were from a health system's EHR on 3884 incident HF cases and 28 903 controls, identified as primary care patients, between May 16, 2000, and May 23, 2013. Recurrent neural network (RNN) models using gated recurrent units (GRUs) were adapted to detect relations among time-stamped events (eg, disease diagnosis, medication orders, procedure orders, etc.) with a 12- to 18-month observation window of cases and controls. Model performance metrics were compared to regularized logistic regression, neural network, support vector machine, and K-nearest neighbor classifier approaches.



Using a 12-month observation window, the area under the curve (AUC) for the RNN model was 0.777, compared to AUCs for logistic regression (0.747), multilayer perceptron (MLP) with 1 hidden layer (0.765), support vector machine (SVM) (0.743), and K-nearest neighbor (KNN) (0.730). When using an 18-month observation window, the AUC for the RNN model increased to 0.883 and was significantly higher than the 0.834 AUC for the best of the baseline methods (MLP).



Deep learning models adapted to leverage temporal relations appear to improve performance of models for detection of incident heart failure with a short observation window of 12-18 months.
ZooKeeper ’ s atomic broadcast protocol : Theory and practice

Apache ZooKeeper is a distributed coordination service for cloud computing, providing essential synchronization and group services for other distributed applications. At its core lies an atomic broadcast protocol, which elects a leader, synchronizes the nodes, and performs broadcasts of updates from the leader. We study the design of this protocol, highlight promised properties, and analyze its official implementation by Apache. In particular, the default leader election protocol is studied in detail.
Software Security Checklist for the Software Life Cycle

A formal approach to security in the software life cycle is essential to protect corporate resources. However, little thought has been given to this aspect of software development. Traditionally, software security has been treated as an afterthought leading to a cycle of ‘penetrate and patch.’ Due to its criticality, security should be integrated as a formal approach in the software life cycle. Both a software security checklist and assessment tools should be incorporated into this life cycle process. The current research at JPL addresses both of these areas through the development of a Software Security Assessment Instrument (SSAI). This paper focuses on the development of a Software Security Checklist (SSC) for the life cycle. It includes the critical areas of requirements gathering and specification, design and code issues, and maintenance and decommissioning of software and systems.
EMERGING TECHNOLOGIES ROBOT ASSISTED LANGUAGE LEARNING

Along with the rapid development of information and communication technologies, educators are trying to keep up with the dramatic changes in our electronic environment. These days mobile technology, with popular devices such as iPhones, Android phones, and iPads, is steering our learning environment towards increasingly focusing on mobile learning or m-Learning. Currently, most interfaces employ keyboards, mouse or touch technology, but some emerging input-interfaces use voiceor marker-based gesture recognition. In the future, one of the cutting-edge technologies likely to be used is robotics. Robots are already being used in some classrooms and are receiving an increasing level of attention. Robots today are developed for special purposes, quite similar to personal computers in their early days. However, in the future, when mass production lowers prices, robots will bring about big changes in our society. In this column, the author focuses on educational service robots. Educational service robots for language learning and robot-assisted language learning (RALL) will be introduced, and the hardware and software platforms for RALL will be explored, as well as implications for future research.
Vulnerability Testing of Software System Using Fault Injection

We describe an approach for testing a software system for possible securi ty flaws. Traditionally, security testing is done using penetration analysis and formal methods. Based on the observation that most security flaws are triggered due to a flawed interaction with the envi ronment, we view the security testing problem as the problem of testing for the fault-tolerance prop erties of a software system. We consider each environment perturbation as a fault and the resulting security ompromise a failure in the toleration of such faults. Our approach is based on the well known techn ique of fault-injection. Environment faults are injected into the system under test and system beha vior observed. The failure to tolerate faults is an indicator of a potential security flaw in the syst em. An Environment-Application Interaction (EAI) fault model is proposed. EAI allows us to decide what f aults to inject. Based on EAI, we present a security-flaw classification scheme. This scheme was used to classif y 142 security flaws in a vulnerability database. This classification revealed that 91% of the security flaws in the database are covered by the EAI model.
Online Detection of Abnormal Events Using Incremental Coding Length

We present an unsupervised approach for abnormal event detection in videos. We propose, given a dictionary of features learned from local spatiotemporal cuboids using the sparse coding objective, the abnormality of an event depends jointly on two factors: the frequency of each feature in reconstructing all events (or, rarity of a feature) and the strength by which it is used in reconstructing the current event (or, the absolute coefficient). The Incremental Coding Length (ICL) of a feature is a measure of its entropy gain. Given a dictionary, the ICL computation does not involve any parameter, is computationally efficient and has been used for saliency detection in images with impressive results. In this paper, the rarity of a dictionary feature is learned online as its average energy, a function of its ICL. The proposed approach is applicable to real world streaming videos. Experiments on three benchmark datasets and evaluations in comparison with a number of mainstream algorithms show that the approach is comparable to the state-of-the-art.
Exploiting Microdata Annotations to Consistently Categorize Product Offers at Web Scale

Semantically annotated data, using markup languages like RDFa and Microdata, has become more and more publicly available in the Web, especially in the area of e-commerce. Thus, a large amount of structured product descriptions are freely available and can be used for various applications, such as product search or recommendation. However, little efforts have been made to analyze the categories of the available product descriptions. Although some products have an explicit category assigned, the categorization schemes vary a lot, as the products originate from thousands of different sites. This heterogeneity makes the use of supervised methods, which have been proposed by most previous works, hard to apply. Therefore, in this paper, we explain how distantly supervised approaches can be used to exploit the heterogeneous category information in order to map the products to set of target categories from an existing product catalogue. Our results show that, even though this task is by far not trivial, we can reach almost 56% accuracy for classifying products into 37 categories.
A novel high-speed and low-power negative voltage level shifter for low voltage applications

A novel high-speed and low-power negative level shifter suitable for low voltage applications is presented. To reduce the switching delay and leakage current, a novel bootstrapping technique is designed for the level shifter. Furthermore, a pull-down driver is proposed to have high driving capability under different operation modes. The circuit has been designed in 130nm 1.5V/5V triple-well CMOS technology with a nominal power supply VDD of 1.5V and a negative voltage of −4.5V. Simulation results show that the switching delay and power consumption have been significantly reduced by roughly 62% and 65%, respectively. In addition, the proposed level shifter realizes a wide operation margin with a lower VDD compared to conventional implementations.
MODEL UNCERTAINTY IN CROSS-COUNTRY GROWTH REGRESSIONS

We investigate the issue of model uncertainty in cross-country growth regressions using Bayesian Model Averaging (BMA). We find that the posterior probability is very spread among many models suggesting the superiority of BMA over choosing any single model. Out-of-sample predictive results support this claim. In contrast with Levine and Renelt (1992), our results broadly support the more “optimistic” conclusion of Sala-i-Martin (1997b), namely that some variables are important regressors for explaining cross-country growth patterns. However, care should be taken in the methodology employed. The approach proposed here is firmly grounded in statistical theory and immediately leads to posterior and predictive inference.
Using Bayes Network for Prediction of Type-2 diabetes

Diabetes mellitus is a chronic disease and a major public health challenge worldwide. Using data mining methods to aid people to predict diabetes has become a popular topic. In this paper, Bayes Network was proposed to predict patients with developing Type-2 diabetes. The dataset used is the Pima Indians Diabetes Data Set, which collects the information of patients with and without developing Type-2 diabetes. Weka software was used throughout this study. Accurate results have been obtained which proves using the proposed Bayes network to predict Type-2 diabetes is effective.
A case study in the gamification of a university-level games development course

Gamification is the application of game mechanics and player incentives to non-game environments. When designed correctly, gamification has been found to increase engagement and encourage targeted behaviours among users. This paper presents the gamification of a university course in Computer Games Development using an online learning management tool, including how this might generalize to other courses.
 Our goal with gamification was to improve lecture attendance, content understanding, problem solving skills and general engagement. The success of this intervention was measured using course marks, lecturer evaluations, lecture attendance, and a questionnaire; all with strongly positive results. However, this must be balanced against the costs, both monetary and time, required to successfully implement gamification.
Identifying Text Polarity Using Random Walks

Automatically identifying the polarity of words is a very important task in Natural Language Processing. It has applications in text classification, text filtering, analysis of product review, analysis of responses to surveys, and mining online discussions. We propose a method for identifying the polarity of words. We apply a Markov random walk model to a large word relatedness graph, producing a polarity estimate for any given word. A key advantage of the model is its ability to accurately and quickly assign a polarity sign and magnitude to any word. The method could be used both in a semi-supervised setting where a training set of labeled words is used, and in an unsupervised setting where a handful of seeds is used to define the two polarity classes. The method is experimentally tested using a manually labeled set of positive and negative words. It outperforms the state of the art methods in the semi-supervised setting. The results in the unsupervised setting is comparable to the best reported values. However, the proposed method is faster and does not need a large corpus.
Electrical Characterization of Highly Efficient, Optically Transparent Nanometers-thick Unit cells for Antenna-on-Display Applications

This paper presents a novel method of characterizing optically transparent diamond-grid unit cells at millimeter-wave (mmWave) spectrum. The unit cell consists of Ag-alloy grids featuring 2000-Å thickness and $3 \mu \mathrm{m}$ grid-width, resulting in 88 % optical transmittance and sheet resistance of $\pmb{3.22 \Omega/\mathrm{sq}}$. The devised characterization method enables accurate and efficient modeling of transparent circuits at mmWave. The validity of this approach is studied by devising an optically transparent patch antenna operating at 30.34 GHz with a measured gain of 3.2 dBi. The featured analysis and demonstration paves way to a novel concept of integrating optically transparent antennas within the active region of display panels in the future.