nils-herrmann/clean_scirepeval huggingface dataset:
RumourEval 2019: Determining Rumour Veracity and Support for Rumours

This is the proposal for RumourEval-2019, which will run in early 2019 as part of that year’s SemEval event. Since the first RumourEval shared task in 2017, interest in automated claim validation has greatly increased, as the dangers of “fake news” have become a mainstream concern. Yet automated support for rumour checking remains in its infancy. For this reason, it is important that a shared task in this area continues to provide a focus for effort, which is likely to increase. We therefore propose a continuation in which the veracity of further rumours is determined, and as previously, supportive of this goal, tweets discussing them are classified according to the stance they take regarding the rumour. Scope is extended compared with the first RumourEval, in that the dataset is substantially expanded to include Reddit as well as Twitter data, and additional languages are also
Predicting crime using Twitter and kernel density estimation

Twitter is used extensively in the United States as well as globally, creating many opportunities to augment decision support systems with Twitterdriven predictive analytics. Twitter is an ideal data source for decision support: its users, who number in the millions, publicly discuss events, emotions, and innumerable other topics; its content is authored and distributed in real time at no charge; and individual messages (also known as tweets) are often tagged with precise spatial and temporal coordinates. This article presents research investigating the use of spatiotemporally tagged tweets for crime prediction. We use Twitter-specific linguistic analysis and statistical topic modeling to automatically identify discussion topics across a major city in the United States. We then incorporate these topics into a crime prediction model and show that, for 19 of the 25 crime types we studied, the addition of Twitter data improves crime prediction performance versus a standard approach based on kernel density estimation. We identify a number of performance bottlenecks that could impact the use of Twitter in an actual decision support system. We also point out important areas of future work for this research, including deeper semantic analysis of message con∗Email address: msg8u@virginia.edu; Tel.: 1+ 434 924 5397;   to Decision Support Systems January 14, 2014 tent, temporal modeling, and incorporation of auxiliary data sources. This research has implications specifically for criminal justice decision makers in charge of resource allocation for crime prevention. More generally, this research has implications for decision makers concerned with geographic spaces occupied by Twitter-using individuals.
Impact of waiting time on tourists satisfaction in a theme park: An empirical investigation

As service organizations have realized that service is fundamental to establish their competitive advantage, waiting time management has been a subject of much service research. This study aims to explore how waiting time impact on tourist satisfaction in theme parks. The findings from a survey of 102 tourists from the theme park in Shenzhen City in China confirm that perceived waiting time, waiting information and waiting environment are significant determinants of tourist satisfaction. Providing waiting information and improving waiting environment are effective ways to enhance tourists' service satisfaction.
How to Build an Inexpensive 5-DOF Haptic Device Using Two Novint Falcons

We demonstrate how two Novint Falcons, inexpensive commercially available haptic devices, can be modified to a create a reconfigurable five-degreeof-freedom (5-DOF) haptic device for less than $500 (including the two Falcons). The device is intended as an educational tool to allow a broader range of students to experience force and torque feedback, rather than the 3-DOF force feedback typical of inexpensive devices. We also explain how to implement a 5-DOF force/torque control system with gravity compensation.
Combining Quantitative and Logical Data Cleaning

Quantitative data cleaning relies on the use of statistical methods to identify and repair data quality problems while logical data cleaning tackles the same problems using various forms of logical reasoning over declarative dependencies. Each of these approaches has its strengths: the logical approach is able to capture subtle data quality problems using sophisticated dependencies, while the quantitative approach excels at ensuring that the repaired data has desired statistical properties. We propose a novel framework within which these two approaches can be used synergistically to combine their respective strengths. We instantiate our framework using (i) metric functional dependencies (metric FDs), a type of dependency that generalizes the commonly used FDs to identify inconsistencies in domains where only large differences in metric data are considered to be a data quality problem, and (ii) repairs that modify the inconsistent data so as to minimize statistical distortion, measured using the Earth Mover’s Distance (EMD). We show that the problem of computing a statistical distortion minimal repair is NP-hard. Given this complexity, we present an efficient algorithm for finding a minimal repair that has a small statistical distortion using EMD computation over semantically related attributes. To identify semantically related attributes, we present a sound and complete axiomatization and an efficient algorithm for testing implication of metric FDs. While the complexity of inference for some other FD extensions is co-NP complete, we show that the inference problem for metric FDs remains linear, as in traditional FDs. We prove that every instance that can be generated by our repair algorithm is set minimal (with no redundant changes). Our experimental evaluation demonstrates that our techniques obtain a considerably lower statistical distortion than existing repair techniques, while achieving similar levels of efficiency. ∗Supported by NSERC BIN (and Szlichta by MITACS).
Least-Squares Estimation of the Integer GPS

The Global Positioning System (GPS) double-difference carrier-phase data are biased by an integer number of cycles. In this contribution a new method is introduced that enables very fast integer least-squares estimation of the ambiguities. The method makes use of an ambiguity transformation that allows one to reformulate the original ambiguity estimation problem as a new problem that is much easier to solve. The transformation aims at decorrelating the least-squares ambiguities and is based on an integer approximation of the conditional least-squares transformation. And through a flattening of the typical discontinuity in the GPS-spectrum of conditional variances of the ambiguities, the transformation returns new ambiguities that show a dramatic improvement in precision in comparison with the original double-difference ambiguities.
Mining the Correlation between Lyrical and Audio Features and the Emergence of Mood

Understanding the mood of music holds great potential for recommendation and genre identification problems. Unfortunately, hand-annotating music with mood tags is usually an expensive, time-consuming and subjective process, to such an extent that automatic mood recognition methods are required. In this paper we present a new unsupervised learning approach for mood recognition, based on the lyrics and the audio of a song. Our system thus eliminates the need for ground truth mood annotations, even for training the system. We hypothesize that lyrics and audio are both partially determined by the mood, and that there are no other strong common effects affecting these aspects of music. Based on this assumption, mood can be detected by performing a multi-modal analysis, identifying what lyrics and audio have in common. We demonstrate the effectiveness of this using Canonical Correlation Analysis, and confirm our hypothesis in a subsequent analysis of the results.
Challenges and opportunities in the purification of recombinant tagged proteins.

The purification of recombinant proteins by affinity chromatography is one of the most efficient strategies due to the high recovery yields and purity achieved. However, this is dependent on the availability of specific affinity adsorbents for each particular target protein. The diversity of proteins to be purified augments the complexity and number of specific affinity adsorbents needed, and therefore generic platforms for the purification of recombinant proteins are appealing strategies. This justifies why genetically encoded affinity tags became so popular for recombinant protein purification, as these systems only require specific ligands for the capture of the fusion protein through a pre-defined affinity tag tail. There is a wide range of available affinity pairs "tag-ligand" combining biological or structural affinity ligands with the respective binding tags. This review gives a general overview of the well-established "tag-ligand" systems available for fusion protein purification and also explores current unconventional strategies under development.
Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-Tuning

Deep neural networks require a large amount of labeled training data during supervised learning. However, collecting and labeling so much data might be infeasible in many cases. In this paper, we introduce a deep transfer learning scheme, called selective joint fine-tuning, for improving the performance of deep learning tasks with insufficient training data. In this scheme, a target learning task with insufficient training data is carried out simultaneously with another source learning task with abundant training data. However, the source learning task does not use all existing training data. Our core idea is to identify and use a subset of training images from the original source learning task whose low-level characteristics are similar to those from the target learning task, and jointly fine-tune shared convolutional layers for both tasks. Specifically, we compute descriptors from linear or nonlinear filter bank responses on training images from both tasks, and use such descriptors to search for a desired subset of training samples for the source learning task. Experiments demonstrate that our deep transfer learning scheme achieves state-of-the-art performance on multiple visual classification tasks with insufficient training data for deep learning. Such tasks include Caltech 256, MIT Indoor 67, and fine-grained classification problems (Oxford Flowers 102 and Stanford Dogs 120). In comparison to fine-tuning without a source domain, the proposed method can improve the classification accuracy by 2% - 10% using a single model. Codes and models are available at https://github.com/ZYYSzj/Selective-Joint-Fine-tuning.
Foreign Direct Investment , Financial Development and Economic Growth : Empirical Evidence from North African Countries

The present paper examines the causal linkage between foreign direct investment (FDI), financial development, and economic growth in a panel of 4 countries of North Africa (Tunisia, Morocco, Algeria and Egypt) over the period 1980-2011. The study moves away from the traditional cross-sectional analysis, and focuses on more direct evidence of the channels through which FDI inflows can promote economic growth of the host country. Using Generalized Method of Moment (GMM) panel data analysis, we find strong evidence of a positive relationship between FDI and economic growth. We also find evidence that the development of the domestic financial system is an important prerequisite for FDI to have a positive effect on economic growth. The policy implications of this study appeared clear. Improvement efforts need to be driven by local-level reforms to ensure the development of domestic financial system in order to maximize the benefits of the presence of FDI.
Functional MRI of language: new approaches to understanding the cortical organization of semantic processing.

Until recently, our understanding of how language is organized in the brain depended on analysis of behavioral deficits in patients with fortuitously placed lesions. The availability of functional magnetic resonance imaging (fMRI) for in vivo analysis of the normal brain has revolutionized the study of language. This review discusses three lines of fMRI research into how the semantic system is organized in the adult brain. These are (a) the role of the left inferior frontal lobe in semantic processing and dissociations from other frontal lobe language functions, (b) the organization of categories of objects and concepts in the temporal lobe, and (c) the role of the right hemisphere in comprehending contextual and figurative meaning. Together, these lines of research broaden our understanding of how the brain stores, retrieves, and makes sense of semantic information, and they challenge some commonly held notions of functional modularity in the language system.
Searching by Talking: Analysis of Voice Queries on Mobile Web Search

The growing popularity of mobile search and the advancement in voice recognition technologies have opened the door for web search users to speak their queries, rather than type them. While this kind of voice search is still in its infancy, it is gradually becoming more widespread. In this paper, we examine the logs of a commercial search engine's mobile interface, and compare the spoken queries to the typed-in queries. We place special emphasis on the semantic and syntactic characteristics of the two types of queries. %Our analysis suggests that voice queries focus more on audio-visual content and question answering, and less on social networking and adult domains. We also conduct an empirical evaluation showing that the language of voice queries is closer to natural language than typed queries. Our analysis reveals further differences between voice and text search, which have implications for the design of future voice-enabled search tools.
Acoustic structures in the alarm calls of Gunnison's prairie dogs.

Acoustic structures of sound in Gunnison's prairie dog alarm calls are described, showing how these acoustic structures may encode information about three different predator species (red-tailed hawk-Buteo jamaicensis; domestic dog-Canis familaris; and coyote-Canis latrans). By dividing each alarm call into 25 equal-sized partitions and using resonant frequencies within each partition, commonly occurring acoustic structures were identified as components of alarm calls for the three predators. Although most of the acoustic structures appeared in alarm calls elicited by all three predator species, the frequency of occurrence of these acoustic structures varied among the alarm calls for the different predators, suggesting that these structures encode identifying information for each of the predators. A classification analysis of alarm calls elicited by each of the three predators showed that acoustic structures could correctly classify 67% of the calls elicited by domestic dogs, 73% of the calls elicited by coyotes, and 99% of the calls elicited by red-tailed hawks. The different distributions of acoustic structures associated with alarm calls for the three predator species suggest a duality of function, one of the design elements of language listed by Hockett [in Animal Sounds and Communication, edited by W. E. Lanyon and W. N. Tavolga (American Institute of Biological Sciences, Washington, DC, 1960), pp. 392-430].
Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences

Recurrent Neural Networks (RNNs) have become the state-of-the-art choice for extracting patterns from temporal sequences. However, current RNN models are ill-suited to process irregularly sampled data triggered by events generated in continuous time by sensors or other neurons. Such data can occur, for example, when the input comes from novel event-driven artificial sensors that generate sparse, asynchronous streams of events or from multiple conventional sensors with different update intervals. In this work, we introduce the Phased LSTM model, which extends the LSTM unit by adding a new time gate. This gate is controlled by a parametrized oscillation with a frequency range that produces updates of the memory cell only during a small percentage of the cycle. Even with the sparse updates imposed by the oscillation, the Phased LSTM network achieves faster convergence than regular LSTMs on tasks which require learning of long sequences. The model naturally integrates inputs from sensors of arbitrary sampling rates, thereby opening new areas of investigation for processing asynchronous sensory events that carry timing information. It also greatly improves the performance of LSTMs in standard RNN applications, and does so with an order-of-magnitude fewer computes at runtime.
Measuring the Hubble Constant with Neutron Star Black Hole Mergers.

The detection of GW170817 and the identification of its host galaxy have allowed for the first standard-siren measurement of the Hubble constant, with an uncertainty of ∼14%. As more detections of binary neutron stars with redshift measurement are made, the uncertainty will shrink. The dominating factors will be the number of joint detections and the uncertainty on the luminosity distance of each event. Neutron star black hole mergers are also promising sources for advanced LIGO and Virgo. If the black hole spin induces precession of the orbital plane, the degeneracy between luminosity distance and the orbital inclination is broken, leading to a much better distance measurement. In addition, neutron star black hole sources are observable to larger distances, owing to their higher mass. Neutron star black holes could also emit electromagnetic radiation: depending on the black hole spin and on the mass ratio, the neutron star can be tidally disrupted, resulting in electromagnetic emission. We quantify the distance uncertainty for a wide range of black hole mass, spin, and orientations and find that the 1σ statistical uncertainty can be up to a factor of ∼10 better than for a nonspinning binary neutron star merger with the same signal-to-noise ratio. The better distance measurement, the larger gravitational-wave detectable volume, and the potentially bright electromagnetic emission imply that spinning black hole neutron star binaries can be the optimal standard-siren sources as long as their astrophysical rate is larger than O(10)  Gpc^{-3} yr^{-1}, a value allowed by current astrophysical constraints.
Toward A Dynamic View of Organizational Ambidexterity : Promoting a Sense of Balance and Contingency

A substantial body of work has examined how exploitative and exploratory learning processes need to be balanced within an organization in order to increase innovation, productivity, and firm performance. Since exploration and exploitation require different resources, structures, and processes, several approaches to balancing these activities have been suggested; one of which is simultaneous implementation which is termed ambidexterity. In this paper, we adjust the lens and suggest that equally crucial issues to resolve are (a) defining ‘balance’ and (b) determining criteria for assessing ‘appropriate.’ We argue that balance does not necessarily require identical proportions of exploration and exploitation and propose different mixes of these two processes leading to different ambidexterity configurations. Three specific ambidexterity configurations are examined in terms of their distinct contributions to strategic objectives. In addition we argue that several contingency factors (organizational and environmental) influence the relation between particular ambidexterity configurations and performance. Therefore an ambidexterity configurations need to change and evolve to achieve optimum performance over time. We contribute to emerging research in contingency theory, organizational learning, and strategic management.
Journalism 2.0: Exploring the Impact of Mobile and Social Media on Journalism Education

Journalism is in crisis (Hall, 2005; Hirst, 2011; McChesney & Nichols, 2010); how does traditional journalism respond to a world where consumer preference is for music that is now distributed via the Internet rather than purchased on CDs, video that is streamed either live or on demand rather than DVD, and news that is distributed via a host of social media channels such as Twitter, YouTube, Facebook and viewed on mobile devices such as the iPad rather than traditional print media? Fairfax media recently announced 1900 job losses within the traditional ABSTRACT
Malware variant detection

Malware programs (e.g., viruses, worms, Trojans, etc.) are a worldwide epidemic. Studies and statistics show that the impact of malware is getting worse. Malware detectors are the primary tools in the defence against malware. Most commercial anti-malware scanners maintain a database of malware patterns and heuristic signatures for detecting malicious programs within a computer system. Malware writers use semantic-preserving code transformation (obfuscation) techniques to produce new stealth variants of their malware programs. Malware variants are hard to detect with today’s detection technologies as these tools rely mostly on syntactic properties and ignore the semantics of malicious executable programs. A robust malware detection technique is required to handle this emerging security
Toponym matching through deep neural networks

Toponym matching, i.e. pairing strings that represent the same real-world location, is a fundamental problemfor several practical applications. The current state-of-the-art relies on string similarity metrics, either specifically developed for matching place names or integrated within methods that combine multiple metrics. However, these methods all rely on common sub-strings in order to establish similarity, and they do not effectively capture the character replacements involved in toponym changes due to transliterations or to changes in language and culture over time. In this article, we present a novel matching approach, leveraging a deep neural network to classify pairs of toponyms as either matching or nonmatching. The proposed network architecture uses recurrent nodes to build representations from the sequences of bytes that correspond to the strings that are to be matched. These representations are then combined and passed to feed-forward nodes, finally leading to a classification decision. We present the results of a wide-ranging evaluation on the performance of the proposed method, using a large dataset collected from the GeoNames gazetteer. These results show that the proposed method can significantly outperform individual similarity metrics from previous studies, as well as previous methods based on supervised machine learning for combining multiple metrics. ARTICLE HISTORY Received 16 May 2017