nils-herrmann/clean_scirepeval huggingface dataset:
Beyond Just Text: Semantic Emoji Similarity Modeling to Support Expressive Communication 👫📲😃

Emoji, a set of pictographic Unicode characters, have seen strong uptake over the last couple of years. All common mobile platforms and many desktop systems now support emoji entry, and users have embraced their use. Yet, we currently know very little about what makes for good emoji entry. While soft keyboards for text entry are well optimized, based on language and touch models, no such information exists to guide the design of emoji keyboards. In this article, we investigate of the problem of emoji entry, starting with a study of the current state of the emoji keyboard implementation in Android. To enable moving forward to novel emoji keyboard designs, we then explore a model for emoji similarity that is able to inform such designs. This semantic model is based on data from 21 million collected tweets containing emoji. We compare this model against a solely description-based model of emoji in a crowdsourced study. Our model shows good perfor mance in capturing detailed relationships between emoji.
Quattroped: A Leg--Wheel Transformable Robot

This paper reports on the design, integration, and performance evaluation of a novel four-leg/four-wheel transformable mobile robot, Quattroped. In contrast to most hybrid platforms that have separate mechanisms and actuators for wheels and legs, this robot is implemented with a unique transformation mechanism that directly switches the morphology of the driving mechanism between the wheels (i.e., a full circle) and 2 degrees of freedom leg (i.e., combining two half circles as a leg), so that the same system of actuation power can be efficiently utilized in both wheeled and legged modes. The design process, mechatronics, software infrastructure, behavioral development, and leg-wheel dynamic characteristics are described. The performance of the robot is evaluated in various scenarios, including driving and turning in wheeled mode, driving, step and bar crossing, irregular terrain passing, and stair climbing in legged mode. Taking advantage of the leg-wheel combination on a single platform, the comparison of the wheeled and legged locomotion is also discussed.
BouldAR: using augmented reality to support collaborative boulder training

Nowadays smartphones are ubiquitous and - to some extent - already used to support sports training, e.g. runners or bikers track their trip with a gps-enabled smartphone. But recent mobile technology has powerful processors that allow even more complex tasks like image or graphics processing. In this work we address the question on how mobile technology can be used for collaborative boulder training. More specifically, we present a mobile augmented reality application to support various parts of boulder training. The proposed approach also incorporates sharing and other social features. Thus our solution supports collaborative training by providing an intuitive way to create, share and define goals and challenges together with friends. Furthermore we propose a novel method of trackable generation for augmented reality. Synthetically generated images of climbing walls are used as trackables for real, existing walls.
Ontologies for the Integration of Air Quality Models and 3D City Models

In the perspective of a sustainable urban planning, it is necessary to investigate cities in a holistic way and to accept surprises in the response of urban environments to a particular set of strategies. For example, the process of inner-city densification may limit air pollution, carbon emissions, and energy use through reduced transportation; on the other hand, the resulting street canyons could lead to local levels of pollution that could be higher than in a low-density urban setting. The holistic approach to sustainable urban planning implies using different models in an integrated way that is capable of simulating the urban system. As the interconnection of such models is not a trivial task, one of the key elements that may be applied is the description of the urban geometric properties in an “interoperable” way. Focusing on air quality as one of the most pronounced urban problems, the geometric aspects of a city may be described by objects such as those defined in CityGML, so that an appropriate air quality model can be applied for estimating the quality of the urban air on the basis of atmospheric flow and chemistry equations. It is generally admitted that an ontology-based approach can provide a generic and robust way to interconnect different models. However, a direct approach, that consists in establishing correspondences between concepts, is not sufficient in the present situation. One has to take into account, among other things, the computations involved in the correspondences between concepts. In this paper we first present theoretical background and motivations for the interconnection of 3D city models and other models related to sustainable development and urban planning. Then we present a practical experiment based on the interconnection of CityGML with an air quality model. Our approach is based on the creation of an ontology of air quality models and on the extension of an ontology of urban planning process (OUPP) that acts as an ontology mediator.
A Novel Profit Maximizing Metric for Measuring Classification Performance of Customer Churn Prediction Models

The interest for data mining techniques has increased tremendously during the past decades, and numerous classification techniques have been applied in a wide range of business applications. Hence, the need for adequate performance measures has become more important than ever. In this paper, a cost-benefit analysis framework is formalized in order to define performance measures which are aligned with the main objectives of the end users, i.e., profit maximization. A new performance measure is defined, the expected maximum profit criterion. This general framework is then applied to the customer churn problem with its particular cost-benefit structure. The advantage of this approach is that it assists companies with selecting the classifier which maximizes the profit. Moreover, it aids with the practical implementation in the sense that it provides guidance about the fraction of the customer base to be included in the retention campaign.
Investigating the querying and browsing behavior of advanced search engine users

One way to help all users of commercial Web search engines be more successful in their searches is to better understand what those users with greater search expertise are doing, and use this knowledge to benefit everyone. In this paper we study the interaction logs of advanced search engine users (and those not so advanced) to better understand how these user groups search. The results show that there are marked differences in the queries, result clicks, post-query browsing, and search success of users we classify as advanced (based on their use of query operators), relative to those classified as non-advanced. Our findings have implications for how advanced users should be supported during their searches, and how their interactions could be used to help searchers of all experience levels find more relevant information and learn improved searching strategies.
Transfer Price-based Money Laundering in International Trade

This paper defines money laundering in the context of international trade and builds analytic models to measure the contribution of transfer prices to international money laundering. Money laundry-related transfer pricing and transfer price-based money laundering are analyzed in detail. It argues that transfer price-based capital flight and tax evasion are variants of money laundering in nature to the extent that they all enable the apparently legal ownership of the property shifted illegally. Our main contribution lies in the identification of the artificial transfer pricing (ATP) paradigm and integrating capital flight and tax evasion into the models to estimate its contribution to global money laundering pool, helping anti-money laundering (AML) policy-makers better understand the nature of transfer pricing and its negative impact upon the economy. It concludes that effective audit and inspection systems should be established in order to better detect suspicious money laundering transactions and prevent money laundering crimes (MLCs)
An algorithm for local geoparsing of microtext

The location of the author of a social media message is not invariably the same as the location that the author writes about in the message. In applications that mine these messages for information such as tracking news, political events or responding to disasters, it is the geographic content of the message rather than the location of the author that is important. To this end, we present a method to geo-parse the short, informal messages known as microtext. Our preliminary investigation has shown that many microtext messages contain place references that are abbreviated, misspelled, or highly localized. These references are missed by standard geo-parsers. Our geo-parser is built to find such references. It uses Natural Language Processing methods to identify references to streets and addresses, buildings and urban spaces, and toponyms, and place acronyms and abbreviations. It combines heuristics, open-source Named Entity Recognition software, and machine learning techniques. Our primary data consisted of Twitter messages sent immediately following the February 2011 earthquake in Christchurch, New Zealand. The algorithm identified location in the data sample, Twitter messages, giving an F statistic of 0.85 for streets, 0.86 for buildings, 0.96 for toponyms, and 0.88 for place abbreviations, with a combined average F of 0.90 for identifying places. The same data run through a geo-parsing standard, Yahoo! Placemaker, yielded an F statistic of zero for streets and buildings (because Placemaker is designed to find neither streets nor buildings), and an F of 0.67 for toponyms.
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

We propose a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for optimization. Our method iteratively transports a set of particles to match the target distribution, by applying a form of functional gradient descent that minimizes the KL divergence. Empirical studies are performed on various real world models and datasets, on which our method is competitive with existing state-of-the-art methods. The derivation of our method is based on a new theoretical result that connects the derivative of KL divergence under smooth transforms with Stein’s identity and a recently proposed kernelized Stein discrepancy, which is of independent interest.
Level Set Segmentation With Multiple Regions

The popularity of level sets for segmentation is mainly based on the sound and convenient treatment of regions and their boundaries. Unfortunately, this convenience is so far not known from level set methods when applied to images with more than two regions. This communication introduces a comparatively simple way how to extend active contours to multiple regions keeping the familiar quality of the two-phase case. We further suggest a strategy to determine the optimum number of regions as well as initializations for the contours
Training with Exploration Improves a Greedy Stack LSTM Parser

We adapt the greedy Stack-LSTM dependency parser of Dyer et al. (2015) to support a training-with-exploration procedure using dynamic oracles (Goldberg and Nivre, 2013) instead of cross-entropy minimization. This form of training, which accounts for model predictions at training time rather than assuming an error-free action history, improves parsing accuracies for both English and Chinese, obtaining very strong results for both languages. We discuss some modifications needed in order to get training with exploration to work well for a probabilistic neural-network dependency parser.
Mobile-robot navigation with complete coverage of unstructured environments

There are some mobile-robot applications that require the complete coverage of an unstructured environment. Examples are humanitarian de-mining and floor-cleaning tasks. A complete-coverage algorithm is then used, a path-planning technique that allows the robot to pass over all points in the environment, avoiding unknown obstacles. Different coverage algorithms exist, but they fail working in unstructured environments. This paper details a complete-coverage algorithm for unstructured environments based on sensor information. Simulation results using a mobile robot validate the proposed approach.
The Role of Aggregation in the Measurement of IT-Related Organizational Innovation

Engineering from the University of Michigan, and a Ph.D. in Information Technologies from the MIT Sloan School of Management. His current research interests include IT adoption and diffusion, management of technology and innovation, software development tools and methods, and real options. He has published in  The extent of organizational innovation with IT, an important construct in the IT innovation literature, has been measured in many different ways. Some measures are more narrowly focused while others aggregate innovative behaviors across a set of innovations or across stages in the assimilation lifecycle within organizations. There appear to be some significant tradeoffs involving aggregation. More aggregated measures can be more robust and generalizable and can promote stronger predictive validity, while less aggregated measures allow more context-specific investigations and can preserve clearer theoretical interpretations. This article begins with a conceptual analysis that identifies the circumstances when these tradeoffs are most likely to favor aggregated measures. It is found that aggregation should be favorable when: (1) the researcher's interest is in general innovation or a model that generalizes to a class of innovations, (2) antecedents have effects in the same direction in all assimilation stages, (3) characteristics of organizations can be treated as constant across the innovations in the study, (4) characteristics of innovations can not be treated as constant across organizations in the study, (5) the set of innovations being aggregated includes substitutes or moderate complements, and (6) sources of noise in the measurement of innovation may be present. The article then presents an empirical study using data on the adoption of software process technologies by 608 US based corporations. This study—which had circumstances quite favorable to aggregation—found that aggregating across three innovations within a technology class more than doubled the variance explained compared to single innovation models. Aggregating across assimilation stages had a slight positive effect on predictive validity. Taken together, these results provide initial confirmation of the conclusions from the conceptual analysis regarding the circumstances favoring aggregation.
Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?

In this paper, we evaluate the generalization power of deep features (ConvNets) in two new scenarios: aerial and remote sensing image classification. We evaluate experimentally ConvNets trained for recognizing everyday objects for the classification of aerial and remote sensing images. ConvNets obtained the best results for aerial images, while for remote sensing, they performed well but were outperformed by low-level color descriptors, such as BIC. We also present a correlation analysis, showing the potential for combining/fusing different ConvNets with other descriptors or even for combining multiple ConvNets. A preliminary set of experiments fusing ConvNets obtains state-of-the-art results for the well-known UCMerced dataset.
Boundary Detection in Music Structure Analysis using Convolutional Neural Networks

The recognition of boundaries, e.g., between chorus and verse, is an important task in music structure analysis. The goal is to automatically detect such boundaries in audio signals so that the results are close to human annotation. In this work, we apply Convolutional Neural Networks to the task, trained directly on mel-scaled magnitude spectrograms. On a representative subset of the SALAMI structural annotation dataset, our method outperforms current techniques in terms of boundary retrieval F -measure at different temporal tolerances: We advance the state-of-the-art from 0.33 to 0.46 for tolerances of±0.5 seconds, and from 0.52 to 0.62 for tolerances of ±3 seconds. As the algorithm is trained on annotated audio data without the need of expert knowledge, we expect it to be easily adaptable to changed annotation guidelines and also to related tasks such as the detection of song transitions.
An Evaluation of the Advantages and Disadvantages of Deterministic Database Systems

Recent proposals for deterministic database system designs argue that deterministic database systems facilitate replication since the same input can be independently sent to two different replicas without concern for replica divergence. In addition, they argue that determinism yields performance benefits due to (1) the introduction of deadlock avoidance techniques, (2) the reduction (or elimination) of distributed commit protocols, and (3) light-weight locking. However, these performance benefits are not universally applicable, and there exist several disadvantages of determinism, including (1) the additional overhead of processing transactions for which it is not known in advance what data will be accessed, (2) an inability to abort transactions arbitrarily (e.g., in the case of database or partition overload), and (3) the increased latency required by a preprocessing layer that ensures that the same input is sent to every replica. This paper presents a thorough experimental study that carefully investigates both the advantages and disadvantages of determinism, in order to give a database user a more complete understanding of which database to use for a given database workload and cluster configuration.
Autopilots for Small Fixed-Wing Unmanned Air Vehicles: A Survey

This paper presents a survey of the autopilot systems for small fixed-wing unmanned air vehicles (UAVs). The UAV flight control basics are introduced first. The radio control system and autopilot control system are then explained from both hardware and software viewpoints. Several typical commercial off-the-shelf autopilot packages are compared in detail. In addition, some research autopilot systems are introduced. Finally, conclusions are made with a summary of the current autopilot market and a remark on the future development.This paper presents a survey of the autopilot systems for small fixed-wing unmanned air vehicles (UAVs). The UAV flight control basics are introduced first. The radio control system and autopilot control system are then explained from both hardware and software viewpoints. Several typical commercial off-the-shelf autopilot packages are compared in detail. In addition, some research autopilot systems are introduced. Finally, conclusions are made with a summary of the current autopilot market and a remark on the future development.
The Impact of Perceived Channel Utilities, Shopping Orientations, and Demographics on the Consumer's Online Buying Behavior

This study proposed and tested a model of consumer online buying behavior. The model posits that consumer online buying behavior is affected by demographics, channel knowledge, perceived channel utilities, and shopping orientations. Data were collected by a research company using an online survey of 999 U.S. Internet users, and were cross-validated with other similar national surveys before being used to test the model. Findings of the study indicated that education, convenience orientation, Página 1 de 20 Psychographics of the Consumers in Electronic Commerce 11/10/01 http://www.ascusc.org/jcmc/vol5/issue2/hairong.html experience orientation, channel knowledge, perceived distribution utility, and perceived accessibility are robust predictors of online buying status (frequent online buyer, occasional online buyer, or non-online buyer) of Internet users. Implications of the findings and directions for future research were discussed.
Interaction, narrative, and drama: Creating an adaptive interactive narrative using performance arts theories

Interactive narratives have been used in a variety of applications, including video games, educational games, and training simulations. Maintaining engagement within such environments is an important problem, because it affects entertainment, motivation, and presence. Performance arts theorists have discussed and formalized many techniques that increase engagement and enhance dramatic content of art productions. While constructing a narrative manually, using these techniques, is acceptable for linear media, using this approach for interactive environments results in inflexible experiences due to the unpredictability of users' actions. Few researchers attempted to develop adaptive interactive narrative experiences. However, developing a quality interactive experience is largely an art process, and many of these adaptive techniques do not encode artistic principles. In this paper, I present a new interactive narrative architecture designed using a set of dramatic techniques that I formulated based on several years of training in film and theatre.
Mining Mid-level Features for Image Classification

Mid-level or semi-local features learnt using class-level information are potentially more distinctive than the traditional low-level local features constructed in a purely bottom-up fashion. At the same time they preserve some of the robustness properties with respect to occlusions and image clutter. In this paper we propose a new and effective scheme for extracting mid-level features for image classification, based on relevant pattern mining. In particular, we mine relevant patterns of local compositions of densely sampled low-level features. We refer to the new set of obtained patterns as Frequent Local Histograms or FLHs. During this process, we pay special attention to keeping all the local histogram information and to selecting the most relevant reduced set of FLH patterns for classification. The careful choice of the visual primitives and an extension to exploit both local and global spatial information allow us to build powerful bag-of-FLH-based image representations. We show that these bag-of-FLHs are more discriminative than traditional bag-of-words and yield state-of-the-art results on various image classification benchmarks, including Pascal VOC.
Mirror neurons responding to the observation of ingestive and communicative mouth actions in the monkey ventral premotor cortex.

In the ventral premotor cortex (area F5) of the monkey there are neurons that discharge both when the monkey performs specific motor actions and when it observes another individual performing a similar action (mirror neurons). Previous studies on mirror neurons concerned hand actions. Here, we describe the mirror responses of F5 neurons that motorically code mouth actions. The results showed that about one-third of mouth motor neurons also discharge when the monkey observes another individual performing mouth actions. The majority of these 'mouth mirror neurons' become active during the execution and observation of mouth actions related to ingestive functions such as grasping, sucking or breaking food. Another population of mouth mirror neurons also discharges during the execution of ingestive actions, but the most effective visual stimuli in triggering them are communicative mouth gestures (e.g. lip smacking). Some also fire when the monkey makes communicative gestures. These findings extend the notion of mirror system from hand to mouth action and suggest that area F5, the area considered to be the homologue of human Broca's area, is also involved in communicative functions.