nils-herrmann/clean_scirepeval huggingface dataset:
Usability evaluation with the cognitive walkthrough

The cognitive walkthrough is a technique for evaluating the design of a user interface, with speciaJ attention to how well the interface supports “exploratory learning,” i.e., first-time use without formal training. The evaluation can be performed by the system’s designers in the e,arly stages of design, before empirical user testing is possible. Early versions of the walkthrough method relied on a detailed series of questions, to be answered on paper or electronic forms. This tutorial presents a simpler method, founded in an understanding of the cognitive theory that describes a user’s interactions with a system. The tutorial refines the method on the basis of recent empirical and theoretical studies of exploratory learning with display-based interfaces. The strengths and limitations of the walkthrough method are considered, and it is placed into the context of a more complete design approach.
A nonparallel hexrotor UAV with faster response to disturbances for precision position keeping

This paper presents a fully actuated UAV platform with a nonparallel design. Standard multirotor UAVs equipped with a number of parallel thrusters would result in underactuation. Fighting horizontal wind would require the robot to tilt its whole body toward the direction of the wind. We propose a hexrotor UAV with nonparallel thrusters which results in faster response to disturbances for precision position keeping. A case study is presented to show that hexrotor with a nonparallel design takes less time to resist wind gust than a standard design. We also give the results of a staged peg-in-hole task that measures the rising time of exerting forces using different actuation mechanisms.
Interactive Modeling of City Layouts using Layers of Procedural Content

In this paper, we present new solutions for the interactive modeling of city layouts that combine the power of procedural modeling with the flexibility of manual modeling. Procedural modeling enables us to quickly generate large city layouts, while manual modeling allows us to hand-craft every aspect of a city. We introduce transformation and merging operators for both topology preserving and topology changing transformations based on graph cuts. In combination with a layering system, this allows intuitive manipulation of urban layouts using operations such as drag and drop, translation, rotation etc. In contrast to previous work, these operations always generate valid, i.e., intersection-free layouts. Furthermore, we introduce anchored assignments to make sure that modifications are persistent even if the whole urban layout is regenerated.
Federated Optimization: Distributed Optimization Beyond the Datacenter

We introduce a new and increasingly relevant setting for distributed optimization in machine learning, where the data defining the optimization are distributed (unevenly) over an extremely large number of nodes, but the goal remains to train a high-quality centralized model. We refer to this setting as Federated Optimization. In this setting, communication efficiency is of utmost importance. A motivating example for federated optimization arises when we keep the training data locally on users’ mobile devices rather than logging it to a data center for training. Instead, the mobile devices are used as nodes performing computation on their local data in order to update a global model. We suppose that we have an extremely large number of devices in our network, each of which has only a tiny fraction of data available totally; in particular, we expect the number of data points available locally to be much smaller than the number of devices. Additionally, since different users generate data with different patterns, we assume that no device has a representative sample of the overall distribution. We show that existing algorithms are not suitable for this setting, and propose a new algorithm which shows encouraging experimental results. This work also sets a path for future research needed in the context of federated optimization.
Collaborative interdisciplinary astrobiology research: a bibliometric study of the NASA Astrobiology Institute

This study aims to undertake a bibliometric investigation of the NASA Astrobiology Institute (NAI) funded research that was published between 2008 and 2012 (by teams of Cooperative Agreement Notice Four and Five). For this purpose, the study creates an inventory of publications co-authored through NAI funding and investigates journal preferences, international and institutional collaboration, and citation behaviors of researchers to reach a better understanding of interdisciplinary and collaborative astrobiology research funded by the NAI. Using the NAI annual reports, 1210 peer-reviewed publications are analyzed. The following conclusions are drawn: (1) NAI researchers prefer publishing in high-impact multidisciplinary journals. (2) Astronomy and astrophysics are the most preferred categories to publish based on Web of Science subject categories. (3) NAI is indeed a virtual institution; researchers collaborate with other researchers outside their organization and in some cases outside the U.S. (4) There are prominent scholars in the NAI co-author network but none of them dominates astrobiology.
DETECTING POTHOLES USING SIMPLE IMAGE PROCESSING TECHNIQUES AND REAL-WORLD FOOTAGE

Potholes are a nuisance, especially in the developing world, and can often result in vehicle damage or physical harm to the vehicle occupants. Drivers can be warned to take evasive action if potholes are detected in real-time. Moreover, their location can be logged and shared to aid other drivers and road maintenance agencies. This paper proposes a vehicle-based computer vision approach to identify potholes using a window-mounted camera. Existing literature on pothole detection uses either theoretically constructed pothole models or footage taken from advantageous vantage points at low speed, rather than footage taken from within a vehicle at speed. A distinguishing feature of the work presented in this paper is that a thorough exercise was performed to create an image library of actual and representative potholes under different conditions, and results are obtained using a part of this library. A model of potholes is constructed using the image library, which is used in an algorithmic approach that combines a road colour model with simple image processing techniques such as a Canny filter and contour detection. Using this approach, it was possible to detect potholes with a precision of 81.8% and recall of 74.4.%.
Cost-Sensitive Semi-Supervised Support Vector Machine

In this paper, we study cost-sensitive semi-supervised learning where many of the training examples are unlabeled and different misclassification errors are associated with unequal costs. This scenario occurs in many real-world applications. For example, in some disease diagnosis, the cost of erroneously diagnosing a patient as healthy is much higher than that of diagnosing a healthy person as a patient. Also, the acquisition of labeled data requires medical diagnosis which is expensive, while the collection of unlabeled data such as basic health information is much cheaper. We propose the CS4VM (Cost-Sensitive Semi-Supervised Support Vector Machine) to address this problem. We show that the CS4VM, when given the label means of the unlabeled data, closely approximates the supervised cost-sensitive SVM that has access to the ground-truth labels of all the unlabeled data. This observation leads to an efficient algorithm which first estimates the label means and then trains the CS4VM with the plug-in label means by an efficient SVM solver. Experiments on a broad range of data sets show that the proposed method is capable of reducing the total cost and is computationally efficient.
Regularized Distance Metric Learning: Theory and Algorithm

In this paper, we examine the generalization error of regularized distance metric learning. We show that with appropriate constraints, the generalization error of regularized distance metric learning could be independent from the dimensionality, making it suitable for handling high dimensional data. In addition, we present an efficient online learning algorithm for regularized distance metric learning. Our empirical studies with data classification and face recognition show that the proposed algorithm is (i) effective for distance metric learning when compared to the state-of-the-art methods, and (ii) efficient and robust for high dimensional data.
Reality-Based Models for Vibration Feedback in Virtual Environments

Reality-based modeling of vibrations has been used to enhance the haptic display of virtual environments for impact events such as tapping, although the bandwidths of many haptic displays make it difficult to accurately replicate the measured vibrations. We propose modifying reality-based vibration parameters through a series of perceptual experiments with a haptic display. We created a vibration feedback model, a decaying sinusoidal waveform, by measuring the acceleration of the stylus of a three degree-of-freedom haptic display as a human user tapped it on several real materials. For some materials, the measured parameters (amplitude, frequency, and decay rate) were greater than the bandwidth of the haptic display; therefore, the haptic device was not capable of actively displaying all the vibration models. A series of perceptual experiments, where human users rated the realism of various parameter combinations, were performed to further enhance the realism of the vibration display for impact events given these limitations. The results provided different parameters than those derived strictly from acceleration data. Additional experiments verified the effectiveness of these modified model parameters by showing that users could differentiate between materials in a virtual environment.
Learning a concept-based document similarity measure

Document similarity measures are crucial components of many text-analysis tasks, including information retrieval, document classification, and document clustering. Conventional measures are brittle: They estimate the surface overlap between documents based on the words they mention and ignore deeper semantic connections. We propose a new measure that assesses similarity at both the lexical and semantic levels, and learns from human judgments how to combine them by using machine-learning techniques. Experiments show that the new measure produces values for documents that are more consistent with people’s judgments than people are with each other. We also use it to classify and cluster large document sets covering different genres and topics, and find that it improves both classification and clustering performance.
Crowd modeling and simulation technologies

As a collective and highly dynamic social group, the human crowd is a fascinating phenomenon that has been frequently studied by experts from various areas. Recently, computer-based modeling and simulation technologies have emerged to support investigation of the dynamics of crowds, such as a crowd's behaviors under normal and emergent situations. This article assesses the major existing technologies for crowd modeling and simulation. We first propose a two-dimensional categorization mechanism to classify existing work depending on the size of crowds and the time-scale of the crowd phenomena of interest. Four evaluation criteria have also been introduced to evaluate existing crowd simulation systems from the point of view of both a modeler and an end-user.
 We have discussed some influential existing work in crowd modeling and simulation regarding their major features, performance as well as the technologies used in this work. We have also discussed some open problems in the area. This article will provide the researchers with useful information and insights on the state of the art of the technologies in crowd modeling and simulation as well as future research directions.
A balanced scorecard based framework for assessing the strategic impacts of ERP systems

Although there is no analytical framework for assessing the organizational benefits of ERP systems, several researchers have indicated that the balanced scorecard (BSC) approach may be an appropriate technique for evaluating the performance of ERP systems. This paper fills this gap in the literature by providing a balanced-scorecard based framework for valuing the strategic contributions of an ERP system. Using a successful SAP implementation by a major international aircraft engine manufacturing and service organization as a case study, this paper illustrates that an ERP system does indeed impacts the business objectives of the firm and derives a new innovative ERP framework for valuing the strategic impacts of ERP systems. The ERP valuation framework, called here an ERP scorecard, integrates the four Kaplan and Norton’s balanced scorecard dimensions with Zuboff’s automate, informate and transformate goals of information systems to provide a practical approach for measuring the contributions and impacts of ERP systems on the strategic goals of the company.
An exploratory study of the pull-based software development model

The advent of distributed version control systems has led to the development of a new paradigm for distributed software development; instead of pushing changes to a central repository, developers pull them from other repositories and merge them locally. Various code hosting sites, notably Github, have tapped on the opportunity to facilitate pull-based development by offering workflow support tools, such as code reviewing systems and integrated issue trackers. In this work, we explore how pull-based software development works, first on the GHTorrent corpus and then on a carefully selected sample of 291 projects. We find that the pull request model offers fast turnaround, increased opportunities for community engagement and decreased time to incorporate contributions. We show that a relatively small number of factors affect both the decision to merge a pull request and the time to process it. We also examine the reasons for pull request rejection and find that technical ones are only a small minority.
Cross-domain activity recognition via transfer learning

In activity recognition, one major challenge is how to reduce the labeling effort one needs to make when recognizing a new set of activities. In this paper, we analyze the possibility of transferring knowledge from the available labeled data on a set of existing activities in one domain to help recognize the activities in another different but related domain. We found that such a knowledge transfer process is possible, provided that the recognized activities from the two domains are related in some way. We develop a bridge between the activities in two domains by learning a similarity function via Web search, under the condition that the sensor readings are from the same feature space. Based on the learned similarity measure, our algorithm interprets the data from the source domain as ‘‘pseudo training data’’ in the target domain with different confidence levels, which are in turn fed into supervised learning algorithms for training the classifier. We show that after using this transfer learning approach, the performance of activity recognition in the new domain is increased several fold as compared to when no knowledge transfer is done. Our algorithm is evaluated on several real-world datasets to demonstrate its effectiveness. In the experiments, our algorithm could achieve a 60% accuracy most of the time with no or very few training data in the target domain, which easily outperforms the supervised learning methods.
Road Traffic Data: Collection Methods and Applications

The mission of the IPTS is to provide customer-driven support to the EU policy-making process by researching science-based responses to policy challenges that have both a socioeconomic and a scientific or technological dimension. Legal Notice Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication. ) Certain mobile telephone operators do not allow access to 00 800 numbers or these calls may be billed.
A Multi-Indicator Approach for Geolocalization of Tweets

Real-time information from microblogs like Twitter is useful for different applications such as market research, opinion mining, and crisis management. For many of those messages, location information is required to derive useful insights. Today, however, only around 1% of all tweets are explicitly geotagged. We propose the first multi-indicator method for determining (1) the location where a tweet was created as well as (2) the location of the user’s residence. Our method is based on various weighted indicators, including the names of places that appear in the text message, dedicated location entries, and additional information from the user profile. An evaluation shows that our method is capable of locating 92% of all tweets with a median accuracy of below 30km, as well as predicting the user’s residence with a median accuracy of below 5.1km. With that level of accuracy, our approach significantly outperforms existing work.
Dynamic resource provisioning in cloud computing: A randomized auction approach

This work studies resource allocation in a cloud market through the auction of Virtual Machine (VM) instances. It generalizes the existing literature by introducing combinatorial auctions of heterogeneous VMs, and models dynamic VM provisioning. Social welfare maximization under dynamic resource provisioning is proven NP-hard, and modeled with a linear integer program. An efficient α-approximation algorithm is designed, with α ~ 2.72 in typical scenarios. We then employ this algorithm as a building block for designing a randomized combinatorial auction that is computationally efficient, truthful in expectation, and guarantees the same social welfare approximation factor α. A key technique in the design is to utilize a pair of tailored primal and dual LPs for exploiting the underlying packing structure of the social welfare maximization problem, to decompose its fractional solution into a convex combination of integral solutions. Empirical studies driven by Google Cluster traces verify the efficacy of the randomized auction.
Transcranial magnetic stimulation and the human brain

Transcranial magnetic stimulation (TMS) is rapidly developing as a powerful, non-invasive tool for studying the human brain. A pulsed magnetic field creates current flow in the brain and can temporarily excite or inhibit specific areas. TMS of motor cortex can produce a muscle twitch or block movement; TMS of occipital cortex can produce visual phosphenes or scotomas. TMS can also alter the functioning of the brain beyond the time of stimulation, offering potential for therapy.
Partial Differential Equations

This article is an extremely rapid survey of the modern theory of partial differential equations (PDEs). Sources of PDEs are legion: mathematical physics, geometry, probability theory, continuum mechanics, optimization theory, etc. Indeed, most of the fundamental laws of the physical sciences are partial differential equations and most papers published in applied math concern PDEs. The following discussion is consequently very broad, but also very shallow, and will certainly be inadequate for any given PDE the reader may care about. The goal is rather to highlight some of the many key insights and unifying principles across the entire subject.
Data Visualization With Multidimensional Scaling

We discuss methodology for multidimensional scaling (MDS) and its implementation in two software systems, GGvis and XGvis. MDS is a visualization technique for proximity data, that is, data in the form of N × N dissimilarity matrices. MDS constructs maps (“configurations,” “embeddings”) in IRk by interpreting the dissimilarities as distances. Two frequent sources of dissimilarities are high-dimensional data and graphs. When the dissimilarities are distances between high-dimensional objects, MDS acts as a (often nonlinear) dimension-reduction technique. When the dissimilarities are shortest-path distances in a graph, MDS acts as a graph layout technique. MDS has found recent attention in machine learning motivated by image databases (“Isomap”). MDS is also of interest in view of the popularity of “kernelizing” approaches inspired by Support Vector Machines (SVMs; “kernel PCA”). This article discusses the following general topics: (1) the stability and multiplicity of MDS solutions; (2) the analysis of structure within and between subsets of objects with missing value schemes in dissimilarity matrices; (3) gradient descent for optimizing general MDS loss functions (“Strain” and “Stress”); (4) a unification of classical (Strain-based) and distance (Stress-based) MDS. Particular topics include the following: (1) blending of automatic optimization with interactive displacement of configuration points to assist in the search for global optima; (2) forming groups of objects with interactive brushing to create patterned missing values in MDS loss functions; (3) optimizing MDS loss functions for large numbers of objects relative to a small set of anchor points (“external unfolding”); and (4) a nonmetric version of classical MDS.
A Fingerprint Orientation Model Based on 2D Fourier Expansion (FOMFE) and Its Application to Singular-Point Detection and Fingerprint Indexing

In this paper, we have proposed a fingerprint orientation model based on 2D Fourier expansions (FOMFE) in the phase plane. The FOMFE does not require prior knowledge of singular points (SPs). It is able to describe the overall ridge topology seamlessly, including the SP regions, even for noisy fingerprints. Our statistical experiments on a public database show that the proposed FOMFE can significantly improve the accuracy of fingerprint feature extraction and thus that of fingerprint matching. Moreover, the FOMFE has a low-computational cost and can work very efficiently on large fingerprint databases. The FOMFE provides a comprehensive description for orientation features, which has enabled its beneficial use in feature-related applications such as fingerprint indexing. Unlike most indexing schemes using raw orientation data, we exploit FOMFE model coefficients to generate the feature vector. Our indexing experiments show remarkable results using different fingerprint databases
Peripheral vision and pattern recognition: a review.

We summarize the various strands of research on peripheral vision and relate them to theories of form perception. After a historical overview, we describe quantifications of the cortical magnification hypothesis, including an extension of Schwartz's cortical mapping function. The merits of this concept are considered across a wide range of psychophysical tasks, followed by a discussion of its limitations and the need for non-spatial scaling. We also review the eccentricity dependence of other low-level functions including reaction time, temporal resolution, and spatial summation, as well as perimetric methods. A central topic is then the recognition of characters in peripheral vision, both at low and high levels of contrast, and the impact of surrounding contours known as crowding. We demonstrate how Bouma's law, specifying the critical distance for the onset of crowding, can be stated in terms of the retinocortical mapping. The recognition of more complex stimuli, like textures, faces, and scenes, reveals a substantial impact of mid-level vision and cognitive factors. We further consider eccentricity-dependent limitations of learning, both at the level of perceptual learning and pattern category learning. Generic limitations of extrafoveal vision are observed for the latter in categorization tasks involving multiple stimulus classes. Finally, models of peripheral form vision are discussed. We report that peripheral vision is limited with regard to pattern categorization by a distinctly lower representational complexity and processing speed. Taken together, the limitations of cognitive processing in peripheral vision appear to be as significant as those imposed on low-level functions and by way of crowding.