nils-herrmann/clean_scirepeval huggingface dataset:
Orchestrating a mixed reality performance

A study of a professional touring mixed reality performance called Desert Rain yields insights into how performers orchestrate players' engagement in an interactive experience. Six players at a time journey through an extended physical and virtual set. Each sees a virtual world projected onto a screen made from a fine water spray. This acts as a traversable interface, supporting the illusion that performers physically pass between real and virtual worlds. Live and video-based observations of Desert Rain, coupled with interviews with players and the production team, have revealed how the performers create conditions for the willing suspension of disbelief, and how they monitor and intervene in the players experience without breaking their engagement. This involves carefully timed performances and “off-face” and “virtual” interventions. In turn, these are supported by the ability to monitor players' physical and virtual activity through asymmetric interfaces.
Rapid word learning under uncertainty via cross-situational statistics.

There are an infinite number of possible word-to-word pairings in naturalistic learning environments. Previous proposals to solve this mapping problem have focused on linguistic, social, representational, and attentional constraints at a single moment. This article discusses a cross-situational learning strategy based on computing distributional statistics across words, across referents, and, most important, across the co-occurrences of words and referents at multiple moments. We briefly exposed adults to a set of trials that each contained multiple spoken words and multiple pictures of individual objects; no information about word-picture correspondences was given within a trial. Nonetheless, over trials, subjects learned the word-picture mappings through cross-trial statistical relations. Different learning conditions varied the degree of within-trial reference uncertainty, the number of trials, and the length of trials. Overall, the remarkable performance of learners in various learning conditions suggests that they calculate cross-trial statistics with sufficient fidelity and by doing so rapidly learn word-referent pairs even in highly ambiguous learning contexts.
Index Selection for OLAP

On-line analytical processing (OLAP) is a recent and important application of database systems. Typically, OLAP data is presented as a multidimensional \data cube." OLAP queries are complex and can take many hours or even days to run, if executed directly on the raw data. The most common method of reducing execution time is to precompute some of the queries into summary tables (subcubes of the data cube) and then to build indexes on these summary tables. In most commercial OLAP systems today, the summary tables that are to be precomputed are picked rst, followed by the selection of the appropriate indexes on them. A trial-and-error approach is used to divide the space available between the summary tables and the indexes. This two-step process can perform very poorly. Since both summary tables and indexes consume the same resource |space | their selection should be done together for the most e cient use of space. In this paper, we give algorithms that automate the selection of summary tables and indexes. In particular, we present a family of algorithms of increasing time complexities, and prove strong performance bounds for them. The algorithms with higher complexities have better performance bounds. However, the increase in the performance bound is diminishing, and we show that an algorithm of moderate complexity can perform fairly close to the optimal. This work was supported by NSF grant IRI{92{23405, ARO grant DAAH04{95{1{0192, and Air Force Contract F33615{93{ 1{1339. Present address of V. Harinarayan and A. Rajaraman: Junglee Corp., Palo Alto, CA.
A Visual Interface for Multivariate Temporal Data: Finding Patterns of Events across Multiple Histories

Finding patterns of events over time is important in searching patient histories, Web logs, news stories, and criminal activities. This paper presents PatternFinder, an integrated interface for query and result-set visualization for search and discovery of temporal patterns within multivariate and categorical data sets. We define temporal patterns as sequences of events with inter-event time spans. PatternFinder allows users to specify the attributes of events and time spans to produce powerful pattern queries that are difficult to express with other formalisms. We characterize the range of queries PatternFinder supports as users vary the specificity at which events and time spans are defined. Pattern Finder's query capabilities together with coupled ball-and-chain and tabular visualizations enable users to effectively query, explore and analyze event patterns both within and across data entities (e.g. patient histories, terrorist groups, Web logs, etc.
Architectural Issues in Software Reuse: It's Not Just the Functionality, It's the Packaging

Effective reuse depends not only on finding and reusing components, but also on the ways those components are combined. The informal folklore of software engineering provides a number of diverse styles for organizing software systems. These styles, or architectures, show how to compose systems from components; different styles expect different kinds of component packaging and different kinds of interactions between the components. Unfortunately, these styles and packaging distinctions are often implicit; as a consequence, components with appropriate functionality may fail to work together. This talk surveys common architectural styles, including important packaging and interaction distinctions, and proposes an approach to the problem of reconciling architectural mismatches.
A simple real-word error detection and correction using local word bigram and trigram

Spelling error is broadly classified in two categories namely non word error and real word error. In this paper a localized real word error detection and correction method is proposed where the scores of bigrams generated by immediate left and right neighbour of the candidate word and the trigram of these three words are combined. A single character position error model is assumed so that if a word W is erroneous then the correct word belongs to the set of real words S generated by single character edit operation on W. The above combined score is calculated also on all members of S. These words are ranked in the decreasing order of the score. By observing the rank and using a rule based approach, the error decision and correction candidates are simultaneously selected. The approach gives comparable accuracy with other existing approaches but is computationally attractive. Since only left and right neighbor are involved, multiple errors in a sentence can also be detected ( if the error occurs in every alternate words ).
Programming by Feedback

This paper advocates a new ML-based programming framework, called Programming by Feedback (PF), which involves a sequence of interactions between the active computer and the user. The latter only provides preference judgments on pairs of solutions supplied by the active computer. The active computer involves two components: the learning component estimates the user’s utility function and accounts for the user’s (possibly limited) competence; the optimization component explores the search space and returns the most appropriate candidate solution. A proof of principle of the approach is proposed, showing that PF requires a handful of interactions in order to solve some discrete and continuous benchmark problems.
Are emoticons good enough to train emotion classifiers of Arabic tweets?

Nowadays, the automatic detection of emotions is employed by many applications across different fields like security informatics, e-learning, humor detection, targeted advertising, etc. Many of these applications focus on social media. In this study, we address the problem of emotion detection in Arabic tweets. We focus on the supervised approach for this problem where a classifier is trained on an already labeled dataset. Typically, such a training set is manually annotated, which is expensive and time consuming. We propose to use an automatic approach to annotate the training data based on using emojis, which are a new generation of emoticons. We show that such an approach produces classifiers that are more accurate than the ones trained on a manually annotated dataset. To achieve our goal, a dataset of emotional Arabic tweets is constructed, where the emotion classes under consideration are: anger, disgust, joy and sadness. Moreover, we consider two classifiers: Support Vector Machine (SVM) and Multinomial Naive Bayes (MNB). The results of the tests show that the automatic labeling approaches using SVM and MNB outperform manual labeling approaches.
Treating umbilical granuloma with topical clobetasol propionate cream at home is as effective as treating it with topical silver nitrate in the clinic.

There is a lack of evidence on the best treatment option for umbilical granuloma. The primary aim of this study was to compare three treatments for umbilical granuloma: standard treatment with topical silver nitrate, clobetasol propionate cream (0.05%) and ethanol wipes. The secondary aim was to evaluate whether the treatment could be successfully administered by a parent at home, rather than in the outpatient clinic.



A total of 109 infants were randomised to one of three groups and 94 infants completed the assigned treatment: 30 infants received standard treatment with silver nitrate (99%) in the outpatient clinic, 30 infants had topical clobetasol propionate cream (0.05%) applied at home, and 34 infants received cleansing with ethanol wipes (82%) at home.



Silver nitrate and clobetasol propionate cream (0.05%) were significantly superior to ethanol wipes, with shorter healing times and higher resolution rates (p = 0.0001). Healing time and resolution rates were identical for silver nitrate and clobetasol propionate cream (0.05%). Mild side effects were occasionally reported, all of which were self-limiting.



Treating umbilical granuloma with topical clobetasol propionate cream (0.05%) at home is as effective as treating it with topical silver nitrate (99%) in the clinic.
Combining Gradient and Albedo Data for Rotation Invariant Classification of 3D Surface Texture

We present a new texture classification scheme which is invariant to surface-rotation. Many texture classification approaches have been presented in the past that are image-rotation invariant, However, image rotation is not necessarily the same as surface rotation. We have therefore developed a classifier that uses invariants that are derived from surface properties rather than image properties. Previously we developed a scheme that used surface gradient (normal) fields estimated using photometric stereo. In this paper we augment these data with albedo information and an also employ an additional feature set: the radial spectrum. We used 30 real textures to test the new classifier. A classification accuracy of 91% was achieved when albedo and gradient 1D polar and radial features were combined. The best performance was also achieved by using 2D albedo and gradient spectra. The classification accuracy is 99%.
Bayesian Pot-Assembly from Fragments as Problems in Perceptual-Grouping and Geometric-Learning

A heretofore unsolved problem of great archaeological importance is the automatic assembly of pots made on a wheel from the hundreds (or thousands) of sherds found at an excavation site. An approach is presented to the automatic estimation of mathematical models of such pots from 3D measurements of sherds. A Bayesian approach is formulated beginning with a description of the complete set of geometric parameters that determine the distribution of the sherd measurement data. Matching of fragments and aligning them geometrically into configurations is based on matching break-curves (curves on a pot surface separating fragments), estimated axis and profile curve pairs for individual fragments and configurations of fragments, and a number of features of groups of break-curves. Pot assembly is a bottom-up maximum likelihood performance-based search. Experiments are illustrated on pots which were broken for the purpose, and on sherds from an archaeological dig located in Petra, Jordan. The performance measure can also be aposteriori probability, and many other types of information can be included, e.g., pot wall thickness, surface color, patterns on the surface, etc. This can also be viewed as the problem of learning a geometric object from an unorganized set of free-form fragments of the object and of clutter, or as a problem of perceptual grouping.
Dynamic Materialized Views

A conventional materialized view blindly materializes and maintains all rows of a view, even rows that are never accessed. We propose a more flexible materialization strategy aimed at reducing storage space and view maintenance costs. A dynamic materialized view selectively materializes only a subset of rows, for example, the most frequently accessed rows. One or more control tables are associated with the view and define which rows are currently materialized. The set of materialized rows can be changed dynamically, either manually or automatically by an internal cache manager using a feedback loop. Dynamic execution plans are generated to decide whether the view is applicable at run time. Experimental results in Microsoft SQL Server show that compared with conventional materialized views, dynamic materialized views greatly reduce storage requirements and maintenance costs while achieving better query performance with improved buffer pool efficiency.
Learning Document Embeddings With CNNs

We propose a new model for unsupervised document embedding. Existing approaches either require complex inference or use recurrent neural networks that are difficult to parallelize. We take a different route and use recent advances in language modeling to develop a convolutional neural network embedding model. This allows us to train deeper architectures that are fully parallelizable. Stacking layers together increases the receptive field allowing each successive layer to model increasingly longer range semantic dependencies within the document. Empirically, we demonstrate superior results on two publicly available benchmarks.
Related paper recommendation to support online-browsing of research papers

An online-browsing support system for research papers has been developed that extracts technical terms from a paper and presents links to useful pages such as those explaining the terms. A method to further use the extracted technical terms is proposed to recommend papers to a user that are related to the paper he or she is browsing. Specifically, the proposed method generates a bipartite graph consisting of papers retrieved by the extracted technical terms, which are called related papers, and technical terms appearing in these related papers. It then ranks the related papers using the HITS algorithm for analyzing the bipartite graph and recommends top-ranked papers to the user. The proposed method was compared with other recommendation methods in terms of effectiveness in an experiment.
Core Losses and Torque Ripple in IPM Machines: Dedicated Modeling and Design Tradeoff

The proper combination of stator and rotor slot numbers is pursued in the design of interior permanent-magnet (IPM) motors with wide constant-power speed range. At high speed, in the flux-weakening region, the arising of stator and rotor iron losses due to magnetomotive-force (MMF) spatial harmonics limits the IPM motor performance. Torque ripple is another problem for this kind of machines, both at low and high speed. The numbers of stator slots and rotor equivalent slots have a major impact on both the loss and ripple aspects. A simplified model is proposed here in order to evaluate both problems with a general approach and point out the possible design tradeoff. With respect to previous models in the literature, both stator and rotor losses are included, and a more comprehensive approach is followed in the description of the rotor MMF harmonics. The model's effectiveness is tested through finite element analysis simulations and some experimental results. The proposed approach is useful for the selection of the IPM machine structure according to the specific requirements of the application.
Computer-generated pen-and-ink illustration

This paper describes the principles of traditional pen-and-ink illustration, and shows how a great number of them can be implemented as part of an automated rendering system. It introduces “stroke textures,” which can be used for achieving both texture and tone with line drawing. Stroke textures also allow resolution-dependent rendering, in which the choice of strokes used in an illustration is appropriately tied to the resolution of the target medium. We demonstrate these techniques using complex architectural models, including Frank Lloyd Wright's “Robie House.”
Adolescence-limited and life-course-persistent antisocial behavior: a developmental taxonomy.

A dual taxonomy is presented to reconcile 2 incongruous facts about antisocial behavior: (a) It shows impressive continuity over age, but (b) its prevalence changes dramatically over age, increasing almost 10-fold temporarily during adolescence. This article suggests that delinquency conceals 2 distinct categories of individuals, each with a unique natural history and etiology: A small group engages in antisocial behavior of 1 sort or another at every life stage, whereas a larger group is antisocial only during adolescence. According to the theory of life-course-persistent antisocial behavior, children's neuropsychological problems interact cumulatively with their criminogenic environments across development, culminating in a pathological personality. According to the theory of adolescence-limited antisocial behavior, a contemporary maturity gap encourages teens to mimic antisocial behavior in ways that are normative and adjustive.
Deep reinforcement learning for building HVAC control

Buildings account for nearly 40% of the total energy consumption in the United States, about half of which is used by the HVAC (heating, ventilation, and air conditioning) system. Intelligent scheduling of building HVAC systems has the potential to significantly reduce the energy cost. However, the traditional rule-based and model-based strategies are often inefficient in practice, due to the complexity in building thermal dynamics and heterogeneous environment disturbances. In this work, we develop a data-driven approach that leverages the deep reinforcement learning (DRL) technique, to intelligently learn the effective strategy for operating the building HVAC systems. We evaluate the performance of our DRL algorithm through simulations using the widely-adopted EnergyPlus tool. Experiments demonstrate that our DRL-based algorithm is more effective in energy cost reduction compared with the traditional rule-based approach, while maintaining the room temperature within desired range.
Point cloud compression based on hierarchical point clustering

In this work we propose an algorithm for compressing the geometry of a 3D point cloud (3D point-based model). The proposed algorithm is based on the hierarchical clustering of the points. Starting from the input model, it performs clustering to the points to generate a coarser approximation, or a coarser level of detail (LOD). Iterating this clustering process, a sequence of LODs are generated, forming an LOD hierarchy. Then, the LOD hierarchy is traversed top down in a width-first order. For each node encountered during the traversal, the corresponding geometric updates associated with its children are encoded, leading to a progressive encoding of the original model. Special efforts are made in the clustering to maintain high quality of the intermediate LODs. As a result, the proposed algorithm achieves both generic topology applicability and good ratedistortion performance at low bitrates, facilitating its applications for low-end bandwidth and/or platform configurations.
Hybrid, Multiresolution Wires with Massless Frictional Contacts

We describe a method for the visual interactive simulation of wires contacting with rigid multibodies. The physical model used is a hybrid combining lumped elements and massless quasistatic representations. The latter is based on a kinematic constraint preserving the total length of the wire along a segmented path which can involve multiple bodies simultaneously and dry frictional contact nodes used for roping, lassoing, and fastening. These nodes provide stick and slide friction along the edges of the contacting geometries. The lumped element resolution is adapted dynamically based on local stability criteria, becoming coarser as the tension increases, and up to the purely kinematic representation. Kinematic segments and contact nodes are added, deleted, and propagated based on contact geometries and dry friction configurations. The method gives a dramatic increase in both performance and robustness because it quickly decimates superfluous nodes without loosing stability, yet adapts to complex configurations with many contacts and high curvature, keeping a fixed, large integration time step. Numerical results demonstrating the performance and stability of the adaptive multiresolution scheme are presented along with an array of representative simulation examples illustrating the versatility of the frictional contact model.
Usability Heuristics for Transactional Web Sites

One of the most recognized methods to evaluate usability in software applications is the heuristic evaluation. In this inspection method, Nielsen's heuristics, are the most widely used evaluation instrument. However, there is evidence in the literature which establishes that these heuristics are not appropriate when they are used to measure the level of usability of new emerging categories of software applications. This paper presents a case study where this concept is verified for Transactional Web Sites. Therefore, given the present limitations, a new set of usability heuristics was proposed following a structured and systematic methodology. Finally, fifteen new usability heuristics were obtained as final product of this research. A validation phase allowed to contrast this new proposal with the Nielsen's principles under a real context, where a heuristic evaluation was performed to a Transactional Web Site. The results established that the new set of usability heuristics, which is presented in this study, provides more accurate and promising results than the current proposal of Nielsen.
An HOG-LBP human detector with partial occlusion handling

By combining Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP) as the feature set, we propose a novel human detection approach capable of handling partial occlusion. Two kinds of detectors, i.e., global detector for whole scanning windows and part detectors for local regions, are learned from the training data using linear SVM. For each ambiguous scanning window, we construct an occlusion likelihood map by using the response of each block of the HOG feature to the global detector. The occlusion likelihood map is then segmented by Mean-shift approach. The segmented portion of the window with a majority of negative response is inferred as an occluded region. If partial occlusion is indicated with high likelihood in a certain scanning window, part detectors are applied on the unoccluded regions to achieve the final classification on the current scanning window. With the help of the augmented HOG-LBP feature and the global-part occlusion handling method, we achieve a detection rate of 91.3% with FPPW= 10−6, 94.7% with FPPW= 10−5, and 97.9% with FPPW= 10−4 on the INRIA dataset, which, to our best knowledge, is the best human detection performance on the INRIA dataset. The global-part occlusion handling method is further validated using synthesized occlusion data constructed from the INRIA and Pascal dataset.