nils-herrmann/clean_scirepeval huggingface dataset:
AIREAL: interactive tactile experiences in free air

AIREAL is a novel haptic technology that delivers effective and expressive tactile sensations in free air, without requiring the user to wear a physical device. Combined with interactive computers graphics, AIREAL enables users to feel virtual 3D objects, experience free air textures and receive haptic feedback on gestures performed in free space. AIREAL relies on air vortex generation directed by an actuated flexible nozzle to provide effective tactile feedback with a 75 degrees field of view, and within an 8.5cm resolution at 1 meter. AIREAL is a scalable, inexpensive and practical free air haptic technology that can be used in a broad range of applications, including gaming, mobile applications, and gesture interaction among many others. This paper reports the details of the AIREAL design and control, experimental evaluations of the device's performance, as well as an exploration of the application space of free air haptic displays. Although we used vortices, we believe that the results reported are generalizable and will inform the design of haptic displays based on alternative principles of free air tactile actuation.
Rethinking Access Control and Authentication for the Home Internet of Things (IoT)

Computing is transitioning from single-user devices to the Internet of Things (IoT), in which multiple users with complex social relationships interact with a single device. Currently deployed techniques fail to provide usable access-control specification or authentication in such settings. In this paper, we begin reenvisioning access control and authentication for the home IoT. We propose that access control focus on IoT capabilities (i. e., certain actions that devices can perform), rather than on a per-device granularity. In a 425-participant online user study, we find stark differences in participants’ desired access-control policies for different capabilities within a single device, as well as based on who is trying to use that capability. From these desired policies, we identify likely candidates for default policies. We also pinpoint necessary primitives for specifying more complex, yet desired, access-control policies. These primitives range from the time of day to the current location of users. Finally, we discuss the degree to which different authentication methods potentially support desired policies.
PULP: A System for Exploratory Search of Scientific Literature

Despite the growing importance of exploratory search, information retrieval (IR) systems tend to focus on lookup search. Lookup searches are well served by optimising the precision and recall of search results, however, for exploratory search this may be counterproductive if users are unable to formulate an appropriate search query. We present a system called PULP that supports exploratory search for scientific literature, though the system can be easily adapted to other types of literature. PULP uses reinforcement learning (RL) to avert the user from context traps resulting from poorly chosen search queries, trading off between exploration (presenting the user with diverse topics) and exploitation (moving towards more specific topics). Where other RL-based systems suffer from the "cold start" problem, requiring sufficient time to adjust to a user's information needs, PULP initially presents the user with an overview of the dataset using temporal topic models. Topic models are displayed in an interactive alluvial diagram, where topics are shown as ribbons that change thickness with a given topics relative prevalence over time. Interactive, exploratory search sessions can be initiated by selecting topics as a starting point.
To Share or Not to Share: Towards Understanding the Antecedents of Participation in IT-Enabled Sharing Services

The search for strategies to mitigate undesirable economic, ecological, and social effects of harmful resource consumption has become an important, socially relevant topic. An obvious starting point for businesses that wish to make value creation more sustainable is to increase the utilization rates of existing resources. Modern social Internet technology is an effective means by which to achieve IT-enabled sharing services, which make idle resource capacity owned by one entity accessible to others who need them but do not want to own them. Successful sharing services require synchronized participation of providers and users of resources. The antecedents of the participation behavior of providers and users has not been systematically addressed by the extant literature. This article therefore proposes a model that explains and predicts the participation behavior in sharing services. Our search for a theoretical foundation revealed the Theory of Planned Behavior as most appropriate lens, because this theory enables us to integrate provider behavior and user behavior as constituents of participation behavior. The model is novel for that it is the first attempt to study the interdependencies between the behavior types in sharing service participation and for that it includes both general and specific determinants of the participation behavior.
Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep CNN

We present an image classification based approach to large scale action recognition from 3D skeleton videos. Firstly, we map the 3D skeleton videos to color images, where the transformed action images are translation-scale invariance and dataset independent. Secondly, we propose a multi-scale deep convolutional neural network (CNN) for the image classification task, which could enhance the temporal frequency adjustment of our model. Even though the action images are very different from natural images, the fine-tune strategy still works well. Finally, we exploit various kinds of data augmentation methods to improve the generalization ability of the network. Experimental results on the largest and most challenging benchmark NTU RGB-D dataset show that our method achieves the state-of-the-art performance and outperforms other methods by a large margin.
Conveying shape and features with image-based relighting

Hand-crafted illustrations are often more effective than photographs for conveying the shape and important features of an object, but they require expertise and time to produce. We describe an image compositing system and user interface that allow an artist to quickly and easily create technical illustrations from a set of photographs of an object taken from the same point of view under variable lighting conditions. Our system uses a novel compositing process in which images are combined using spatially-varying light mattes, enabling the final lighting in each area of the composite to be manipulated independently. We describe an interface that provides for the painting of local lighting effects (e.g. shadows, highlights, and tangential lighting to reveal texture) directly onto the composite. We survey some of the techniques used in illustration and lighting design to convey the shape and features of objects and describe how our system can be used to apply these techniques.
Mining periodic behaviors for moving objects

Periodicity is a frequently happening phenomenon for moving objects. Finding periodic behaviors is essential to understanding object movements. However, periodic behaviors could be complicated, involving multiple interleaving periods, partial time span, and spatiotemporal noises and outliers.
 In this paper, we address the problem of mining periodic behaviors for moving objects. It involves two sub-problems: how to detect the periods in complex movement, and how to mine periodic movement behaviors. Our main assumption is that the observed movement is generated from multiple interleaved periodic behaviors associated with certain reference locations. Based on this assumption, we propose a two-stage algorithm, Periodica, to solve the problem. At the first stage, the notion of observation spot is proposed to capture the reference locations. Through observation spots, multiple periods in the movement can be retrieved using a method that combines Fourier transform and autocorrelation. At the second stage, a probabilistic model is proposed to characterize the periodic behaviors. For a specific period, periodic behaviors are statistically generalized from partial movement sequences through hierarchical clustering. Empirical studies on both synthetic and real data sets demonstrate the effectiveness of our method.
Real-Time Crisis Mapping of Natural Disasters Using Social Media

The proposed social media crisis mapping platform for natural disasters uses locations from gazetteer, street map, and volunteered geographic information (VGI) sources for areas at risk of disaster and matches them to geoparsed real-time tweet data streams. The authors use statistical analysis to generate real-time crisis maps. Geoparsing results are benchmarked against existing published work and evaluated across multilingual datasets. Two case studies compare five-day tweet crisis maps to official post-event impact assessment from the US National Geospatial Agency (NGA), compiled from verified satellite and aerial imagery sources.
Image clustering with tensor representation

We consider the problem of image representation and clustering. Traditionally, an n1 x n2 image is represented by a vector in the Euclidean space ℝ n1 x n2. Some learning algorithms are then applied to these vectors in such a high dimensional space for dimensionality reduction, classification, and clustering. However, an image is intrinsically a matrix, or the second order tensor. The vector representation of the images ignores the spatial relationships between the pixels in an image. In this paper, we introduce a tensor framework for image analysis. We represent the images as points in the tensor space Rn1 mathcal Rn2 which is a tensor product of two vector spaces. Based on the tensor representation, we propose a novel image representation and clustering algorithm which explicitly considers the manifold structure of the tensor space. By preserving the local structure of the data manifold, we can obtain a tensor subspace which is optimal for data representation in the sense of local isometry. We call it TensorImage approach. Traditional clustering algorithm such as k-means is then applied in the tensor subspace. Our algorithm shares many of the data representation and clustering properties of other techniques such as Locality Preserving Projections, Laplacian Eigenmaps, and spectral clustering, yet our algorithm is much more computationally efficient. Experimental results show the efficiency and effectiveness of our algorithm.
Advanced grid requirements for the integration of wind turbines into the German transmission system

In Germany 18 GW wind power will have been installed by the end of 2005. Until 2020, this figure reaches the 50 GW mark. Based on the results of recent studies and on the experience with existing wind projects modification of the existing grid code for connection and operation of wind farms in the high voltage grid is necessary. The paper discusses main issues of the suggested requirements by highlighting major changes and extensions. The topics considered are fault ride-through, grid voltage maintenance respective voltage control, system monitoring and protection as well as retrofitting of old units. The new requirements are defined taking into account some new developments in wind turbine technologies which should be utilized in the future to meet grid requirement. Monitoring and system protection is defined under the aspect of sustainability of the measures introduced
Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data

Classification with big data has become one of the latest trends when talking about learning from the available information. The data growth in the last years has rocketed the interest in effectively acquiring knowledge to analyze and predict trends. The variety and veracity that are related to big data introduce a degree of uncertainty that has to be handled in addition to the volume and velocity requirements. This data usually also presents what is known as the problem of classification with imbalanced datasets, a class distribution where the most important concepts to be learned are presented by a negligible number of examples in relation to the number of examples from the other classes. In order to adequately deal with imbalanced big data we propose the Chi-FRBCS-BigDataCS algorithm, a fuzzy rule based classification system that is able to deal with the uncertainly that is introduced in large volumes of data without disregarding the learning in the underrepresented class. The method uses the MapReduce framework to distribute the computational operations of the fuzzy model while it includes cost-sensitive learning techniques in its design to address the imbalance that is present in the data. The good performance of this approach is supported by the experimental analysis that is carried out over twenty-four imbalanced big data cases of study. The results obtained show that the proposal is able to handle these problems obtaining competitive results both in the classification performance of the model and the time needed for the computation.
The Redesign of the Matching Market for American Physicians: Some Engineering Aspects of Economic Design.

We report on the design of the new clearinghouse adopted by the National Resident Matching Program, which annually fills approximately 20,000 jobs for new physicians. Because the market has complementarities between applicants and between positions, the theory of simple matching markets does not apply directly. However, computational experiments show the theory provides good approximations. Furthermore, the set of stable matchings, and the opportunities for strategic manipulation, are surprisingly small. A new kind of "core convergence" result explains this; that each applicant interviews only a small fraction of available positions is important. We also describe engineering aspects of the design process.
Active and passive contributions to spatial learning.

It seems intuitively obvious that active exploration of a new environment will lead to better spatial learning than will passive exposure. However, the literature on this issue is decidedly mixed-in part, because the concept itself is not well defined. We identify five potential components of active spatial learning and review the evidence regarding their role in the acquisition of landmark, route, and survey knowledge. We find that (1) idiothetic information in walking contributes to metric survey knowledge, (2) there is little evidence as yet that decision making during exploration contributes to route or survey knowledge, (3) attention to place-action associations and relevant spatial relations contributes to route and survey knowledge, although landmarks and boundaries appear to be learned without effort, (4) route and survey information are differentially encoded in subunits of working memory, and (5) there is preliminary evidence that mental manipulation of such properties facilitates spatial learning. Idiothetic information appears to be necessary to reveal the influence of attention and, possibly, decision making in survey learning, which may explain the mixed results in desktop virtual reality. Thus, there is indeed an active advantage in spatial learning, which manifests itself in the task-dependent acquisition of route and survey knowledge.
Drivers of agile software development use: Dialectic interplay between benefits and hindrances

Agile software development with its emphasis on producing working code through frequent releases, extensive client interactions and iterative development has emerged as an alternative to traditional plan-based software development methods. While a number of case studies have provided insights into the use and consequences of agile, few empirical studies have examined the factors that drive the adoption and use of agile.  We draw on intention-based theories and a dialectic perspective to identify factors driving the use of agile practices among adopters of this software development methodology.  Data for the study was gathered through an anonymous online survey of software development professionals. We requested participation from members of a selected list of online discussion groups, and received 98 responses.  Our analyses reveal that subjective norm and training play a significant role in influencing software developers’ use of agile processes and methods, while perceived benefits and perceived limitations are not primary drivers of agile use among adopters. Interestingly, perceived benefit emerges as a significant predictor of agile use only if adopters face hindrances to their agile practices.  We conclude that research in the adoption of software development innovations should examine the effects of both enabling and detracting factors and the interactions between them. Since training, subjective norm, and the interplay between perceived benefits and perceived hindrances appear to be key factors influencing the adoption of agile methods, researchers can focus on how to (a) perform training on agile methods more effectively, (b) facilitate the dialog between developers and managers about perceived benefits and hindrances, and (c) capitalize on subjective norm to publicize the benefits of agile methods within an organization. Further, when managing the transition to new software development methods, we recommend that practitioners adapt their strategies and tactics contingent on the extent of perceived hindrances to the change.
Battery powered BION FES network

The Alfred Mann Foundation is completing development of a coordinated network of BION/spl reg/ microstimulator/sensor (hereinafter implant) that has broad stimulating, sensing and communication capabilities. The network consists of a master control unit (MCU) in communication with a group of BION implants. Each implant is powered by a custom lithium-ion rechargeable 10 mW-hr battery. The charging, discharging, safety, stimulating, sensing, and communication circuits are designed to be highly efficient to minimize energy use and maximize battery life and time between charges. The stimulator can be programmed to deliver pulses in any value in the following range: 5 /spl mu/A to 20 mA in 3.3% constant current steps, 7 /spl mu/s to 2000 /spl mu/s in 7 /spl mu/s pulse width steps, and 1 to 4000 Hz in frequency. The preamp voltage sensor covers the range 10 /spl mu/V to 1.0 V with bandpass filtering and several forms of data analysis. The implant also contains sensors that can read out pressure, temperature, DC magnetic field, and distance (via a low frequency magnetic field) up to 20 cm between any two BION implants. The MCU contains a microprocessor, user interface, two-way communication system, and a rechargeable battery. The MCU can command and interrogate in excess of 800 BlON implants every 10 ms, i.e., 100 times a second.
Acoustic barcodes: passive, durable and inexpensive notched identification tags

We present acoustic barcodes, structured patterns of physical notches that, when swiped with e.g., a fingernail, produce a complex sound that can be resolved to a binary ID. A single, inexpensive contact microphone attached to a surface or object is used to capture the waveform. We present our method for decoding sounds into IDs, which handles variations in swipe velocity and other factors. Acoustic barcodes could be used for information retrieval or to triggering interactive functions. They are passive, durable and inexpensive to produce. Further, they can be applied to a wide range of materials and objects, including plastic, wood, glass and stone. We conclude with several example applications that highlight the utility of our approach, and a user study that explores its feasibility.
Rule Based POS Tagger for Marathi Text

Part-of-Speech (POS) tagging is the process of assigning a part-of-speech like noun, verb, adjective, adverb, or other lexical class marker to each word in a sentence. This paper presents a POS Tagger for Marathi language text using Rule based approach, which will assign part of speech to the words in a sentence given as an input. We describe our system as the one which tokenizes the string into tokens and then comparing tokens with the WordNet to assign their particular tags. There are many ambiguous words in Marathi language and we resolve the ambiguity of these words using Marathi grammar rules. KeywordsPOS-Part Of Speech, WordNet, Tagset, Corpus.
Spanish Multicenter Normative Studies (NEURONORMA Project): norms for verbal fluency tests.

Lexical fluency tests are frequently used in clinical practice to assess language and executive function. As part of the Spanish multicenter normative studies (NEURONORMA project), we provide age- and education-adjusted norms for three semantic fluency tasks (animals, fruit and vegetables, and kitchen tools), three formal lexical tasks (words beginning with P, M, and R), and three excluded letter fluency tasks (excluded A, E, and S). The sample consists of 346 participants who are cognitively normal, community dwelling, and ranging in age from 50 to 94 years. Tables are provided to convert raw scores to age-adjusted scaled scores. These were further converted into education-adjusted scaled scores by applying regression-based adjustments. The current norms should provide clinically useful data for evaluating elderly Spanish people. These data may also be of considerable use for comparisons with other international normative studies. Finally, these norms should help improve the interpretation of verbal fluency tasks and allow for greater diagnostic accuracy.
A Statistical Method for 3D Object Detection Applied to Faces and Cars

In this paper, we describe a statistical method for 3D object detection. We represent the statistics of both object appearance and " non-object " appearance using a product of histo-grams. Each histogram represents the joint statistics of a subset of wavelet coefficients and their position on the object. Our approach is to use many such histograms representing a wide variety of visual attributes. Using this method, we have developed the first algorithm that can reliably detect human faces with out-of-plane rotation and the first algorithm that can reliably detect passenger cars over a wide range of viewpoints.
AC Versus DC Distribution SystemsDid We Get it Right?

We presently enjoy a predominantly AC electrical distribution system, the engineering basis for which was designed over 100 years ago. While AC distribution systems have served us well, we should periodically pause to assess what opportunities we have accepted or been denied by the overwhelming predominance of AC electrical power distribution systems. What opportunities could be obtained by engineering DC distribution into at least portions of our present system? What advantages of the present AC distribution system should be recognized and protected? This paper will focus on distribution within premise and low-voltage distribution systems. Specifically, we will address the conversion efficiency costs of adopting various premise AC and DC distribution system topologies. According to a simple predictive model formulated in this paper, premise residential DC distribution will incur unfavorable total conversion efficiency compared with existing AC premise distribution. However, if a residence is supplied by a fuel cell or another DC generator, the total conversion efficiency within a residential DC distribution system could be similar to, or even better than, that for AC distribution.