nils-herrmann/clean_scirepeval huggingface dataset:
Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups.

Qualitative research explores complex phenomena encountered by clinicians, health care providers, policy makers and consumers. Although partial checklists are available, no consolidated reporting framework exists for any type of qualitative design.



To develop a checklist for explicit and comprehensive reporting of qualitative studies (in depth interviews and focus groups).



We performed a comprehensive search in Cochrane and Campbell Protocols, Medline, CINAHL, systematic reviews of qualitative studies, author or reviewer guidelines of major medical journals and reference lists of relevant publications for existing checklists used to assess qualitative studies. Seventy-six items from 22 checklists were compiled into a comprehensive list. All items were grouped into three domains: (i) research team and reflexivity, (ii) study design and (iii) data analysis and reporting. Duplicate items and those that were ambiguous, too broadly defined and impractical to assess were removed.



Items most frequently included in the checklists related to sampling method, setting for data collection, method of data collection, respondent validation of findings, method of recording data, description of the derivation of themes and inclusion of supporting quotations. We grouped all items into three domains: (i) research team and reflexivity, (ii) study design and (iii) data analysis and reporting.



The criteria included in COREQ, a 32-item checklist, can help researchers to report important aspects of the research team, study methods, context of the study, findings, analysis and interpretations.
Quantifying the value of pronunciation lexicons for keyword search in lowresource languages

This paper quantifies the value of pronunciation lexicons in large vocabulary continuous speech recognition (LVCSR) systems that support keyword search (KWS) in low resource languages. State-of-the-art LVCSR and KWS systems are developed for conversational telephone speech in Tagalog, and the baseline lexicon is augmented via three different grapheme-to-phoneme models that yield increasing coverage of a large Tagalog word-list. It is demonstrated that while the increased lexical coverage - or reduced out-of-vocabulary (OOV) rate - leads to only modest (ca 1%-4%) improvements in word error rate, the concomitant improvements in actual term weighted value are as much as 60%. It is also shown that incorporating the augmented lexicons into the LVCSR system before indexing speech is superior to using them post facto, e.g., for approximate phonetic matching of OOV keywords in pre-indexed lattices. These results underscore the disproportionate importance of automatic lexicon augmentation for KWS in morphologically rich languages, and advocate for using them early in the LVCSR stage.
Document Context Neural Machine Translation with Memory Networks

We present a document-level neural machine translation model which takes both source and target document context into account using memory networks. We model the problem as a structured prediction problem with interdependencies among the observed and hidden variables, i.e., the source sentences and their unobserved target translations in the document. The resulting structured prediction problem is tackled with a neural translation model equipped with two memory components, one each for the source and target side, to capture the documental interdependencies. We train the model endto-end, and propose an iterative decoding algorithm based on block coordinate descent. Experimental results of English translations from French, German, and Estonian documents show that our model is effective in exploiting both source and target document context, and statistically significantly outperforms the previous work in terms of BLEU and METEOR.
Tracking ship traffic with Space-Based AIS: Experience gained in first months of operations

The Automatic Identification System (AIS) is based on VHF radio transmissions of ships' identity, position, speed and heading, in addition to other key parameters. In 2004, the Norwegian Defence Research Establishment (FFI) undertook studies to evaluate if the AIS signals could be detected in low Earth orbit. Since then, the interest in Space-Based AIS reception has grown significantly, and both public and private sector organizations have established programs to study the issue, and demonstrate such a capability in orbit. FFI is conducting two such programs. The objective of the first program was to launch a nano-satellite equipped with an AIS receiver into a near polar orbit, to demonstrate Space-Based AIS reception at high latitudes. The satellite was launched from India 12th July 2010. Even though the satellite has not finished commissioning, the receiver is operated with real-time transmission of received AIS data to the Norwegian Coastal Administration. The second program is an ESA-funded project to operate an AIS receiver on the European Columbus module of the International Space Station. Mounting of the equipment, the NORAIS receiver, was completed in April 2010. Currently, the AIS receiver has operated for more than three months, picking up several million AIS messages from more than 60 000 ship identities. In this paper, we will present experience gained with the space-based AIS systems, highlight aspects of tracking ships throughout their voyage, and comment on possible contributions to port security.
Predicting Armed Conflict , 2010 – 2050

The paper predicts changes in global and regional incidences of armed conflict for the 2010–2050 period. The predictions are based on a dynamic multinomial logit model estimation on a 1970–2009 cross-sectional dataset of changes between no armed conflict, minor conflict, and major conflict. Core exogenous predictors are population size, infant mortality rates, demographic composition, education levels, oil dependence, ethnic cleavages, and neighborhood characteristics. Predictions are obtained through simulating the behavior of the conflict variable implied by the estimates from this model. We use projections for the 2011–2050 period for the predictors from the UN World Population Prospects and the International Institute for Applied Systems Analysis. We treat conflicts, recent conflict history, and neighboring conflicts as endogenous variables. Out-of-sample validation of predictions for 2007–2009 (based on estimates for the 1970–2000 period) indicates that the model predicts well, with an AUC of 0.937. Using a p > 0.30 threshold for positive prediction, the True Positive Rate 7–9 years into the future is 0.79 and the False Positive Rate 0.085. We predict a continued decline in the proportion of the world’s countries that have internal armed conflict, from about 15% in 2009 to 7% in 2050. The decline is particularly strong in the Western Asia and North Africa region, and less clear in Africa South of Sahara. The remaining conflict countries will increasingly be concentrated in East, Central, and Southern Africa and in East and South Asia. ∗An earlier version of this paper was presented to the ISA Annual Convention 2009, New York, 15–18 Feb. The research was funded by the Norwegian Research Council grant no. 163115/V10. Thanks to Ken Benoit, Mike Colaresi, Scott Gates, Nils Petter Gleditsch, Joe Hewitt, Bjørn Høyland, Andy Mack, Näıma Mouhleb, Gerald Schneider, and Phil Schrodt for valuable comments.
RPL under mobility

This paper focuses on routing for vehicles getting access to infrastructure either directly or via multiple hops though other vehicles. We study Routing Protocol for Low power and lossy networks (RPL), a tree-based routing protocol designed for sensor networks. Many design elements from RPL are transferable to the vehicular environment. We provide a simulation performance study of RPL and RPL tuning in VANETs. More specifically, we seek to study the impact of RPL's various parameters and external factors (e.g., various timers and speeds) on its performance and obtain insights on RPL tuning for its use in VANETs.
Leveraging SDN to conserve energy in WSN-An analysis

Energy conservation is one of the serious problems faced by WSN as the sensor nodes have limited battery power and are expected to perform data aggregation and actuation functions in addition to sensing data. Literature has plenty of solutions proposed to reduce energy consumption and usage. With the recent upcoming technology of introducing network programmability that centralizes network management tasks using software defined architecture (SDN), network trafficking is a prominent domain for applicability of SDN. Inherent traffic issues in WSN like data forwarding, aggregation of the data, path break and energy consumption can be efficiently handled by SDN, which provides a platform in which the data plane and the control plane are separated. By integrating SDN in WSN, the sensor nodes perform only forwarding and don't take any routing decision, due to which energy usage will be reduced. We propose a general framework for a software-defined wireless sensor network where the controller will be implemented at the base station, centre nodes in the cluster acts as switches and communication between the switch and the controller is via OpenFlow protocol. We realize the energy saving in the proposed architecture with the results obtained using NS2 and mininet emulator environments.
Describing human decisions in agent-based models - ODD + D, an extension of the ODD protocol

Representing human decisions is of fundamental importance in agent-based models. However, the rationale for choosing a particular human decision model is often not sufficiently empirically or theoretically substantiated in the model documentation. Furthermore, it is difficult to compare models because the model descriptions are often incomplete, not transparent and difficult to understand. Therefore, we expand and refine the ‘ODD’ (Overview, Design Concepts and Details) protocol to establish a standard for describing ABMs that includes human decision-making (ODD+D). Because the ODD protocol originates mainly from an ecological perspective, some adaptations are necessary to better capture human decision-making. We extended and rearranged the design concepts and related guiding questions to differentiate and describe decision-making, adaptation and learning of the agents in a comprehensive and clearly structured way. The ODD+D protocol also incorporates a section on ‘Theoretical and Empirical Background’ to encourage model designs and model assumptions that are more closely related to theory. The application of the ODD+D protocol is illustrated with a description of a social-ecological ABM on water use. Although the ODD+D protocol was developed on the basis of example implementations within the socio-ecological scientific community, we believe that the ODD+D protocol may prove helpful for describing ABMs in general when human decisions are included.
Performance analysis and design optimization of LDPC-coded MIMO OFDM systems

We consider the performance analysis and design optimization of low-density parity check (LDPC) coded multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems for high data rate wireless transmission. The tools of density evolution with mixture Gaussian approximations are used to optimize irregular LDPC codes and to compute minimum operational signal-to-noise ratios (SNRs) for ergodic MIMO OFDM channels. In particular, the optimization is done for various MIMO OFDM system configurations, which include a different number of antennas, different channel models, and different demodulation schemes; the optimized performance is compared with the corresponding channel capacity. It is shown that along with the optimized irregular LDPC codes, a turbo iterative receiver that consists of a soft maximum a posteriori (MAP) demodulator and a belief-propagation LDPC decoder can perform within 1 dB from the ergodic capacity of the MIMO OFDM systems under consideration. It is also shown that compared with the optimal MAP demodulator-based receivers, the receivers employing a low-complexity linear minimum mean-square-error soft-interference-cancellation (LMMSE-SIC) demodulator have a small performance loss (< 1dB) in spatially uncorrelated MIMO channels but suffer extra performance loss in MIMO channels with spatial correlation. Finally, from the LDPC profiles that already are optimized for ergodic channels, we heuristically construct small block-size irregular LDPC codes for outage MIMO OFDM channels; as shown from simulation results, the irregular LDPC codes constructed here are helpful in expediting the convergence of the iterative receivers.
Public Management Reform and Economic and Social Development

Most OECD Member countries have sought to renew their systems and structures of public management in the last 10-15 years. Some started earlier than others and the emphasis will vary among Member countries according to their historic traditions and institutions. There is no single best model of public management, but what stands out most clearly is the extent to which countries have pursued and are pursuing broadly common approaches to public management reform. This is most probably because countries have been responding to essentially similar pressures to reform.
Incremental gradient on the Grassmannian for online foreground and background separation in subsampled video

It has recently been shown that only a small number of samples from a low-rank matrix are necessary to reconstruct the entire matrix. We bring this to bear on computer vision problems that utilize low-dimensional subspaces, demonstrating that subsampling can improve computation speed while still allowing for accurate subspace learning. We present GRASTA, Grassmannian Robust Adaptive Subspace Tracking Algorithm, an online algorithm for robust subspace estimation from randomly subsampled data. We consider the specific application of background and foreground separation in video, and we assess GRASTA on separation accuracy and computation time. In one benchmark video example [16], GRASTA achieves a separation rate of 46.3 frames per second, even when run in MATLAB on a personal laptop.
Automating regression testing for evolving GUI software

With the widespread deployment of broadband connections worldwide, software development and maintenance are increasingly being done by multiple engineers, often working around-theclock to maximize code churn rates. To ensure rapid quality assurance of such software, techniques such as “nightly/daily building and smoke testing” have become widespread since they often reveal bugs early in the software development process. During these builds, a development version of the software is checked out from the source code repository tree, compiled, linked, and (re)tested with the goal of (re)validating its basic functionality. Although successful for conventional software, smoke tests are difficult to develop and automatically rerun for software that has a graphical user interface (GUI). In this paper, we describe a framework called DART (Daily Automated Regression Tester) that addresses the needs of frequent and automated re-testing of GUI software. The key to our success is automation: DART automates everything from structural GUI analysis, smoke test case generation, test oracle creation, to code instrumentation, test execution, coverage evaluation, regeneration of test cases, and their re-execution. Together with the operating system’s task scheduler, DART can execute frequently with little input from the developer/tester to retest the GUI software. We provide results of experiments showing the time taken and memory required for GUI analysis, test case and test oracle generation, and test execution. We empirically compare the relative costs of employing different levels of detail in the GUI test oracle. We also show the events and statements covered by the smoke test cases.
Chapter 1 Computational Modelling of Music Cognition and Musical Creativity

This chapter is about computational modelling of the process of musical composition, based on a cognitive model of human behaviour. The idea is to try to study not only the requirements for a computer system which is capable of musical composition, but also to relate it to human behaviour during the same process, so that it may, perhaps, work in the same way as a human composer, but also so that it may, more likely, help us understand how human composers work. Pearce et al. (2002) give a fuller discussion of the motivations behind this endeavour.
LOF: Identifying Density-Based Local Outliers

For many KDD applications, such as detecting criminal activities in E-commerce, finding the rare instances or the outliers, can be more interesting than finding the common patterns. Existing work in outlier detection regards being an outlier as a binary property. In this paper, we contend that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier. This degree is called the local outlier factor (LOF) of an object. It is local in that the degree depends on how isolated the object is with respect to the surrounding neighborhood. We give a detailed formal analysis showing that LOF enjoys many desirable properties. Using real-world datasets, we demonstrate that LOF can be used to find outliers which appear to be meaningful, but can otherwise not be identified with existing approaches. Finally, a careful performance evaluation of our algorithm confirms we show that our approach of finding local outliers can be practical.
NetScore: Towards Universal Metrics for Large-scale Performance Analysis of Deep Neural Networks for Practical Usage

Much of the focus in the design of deep neural networks has been on improving accuracy, leading to more powerful yet highly complex network architectures that are difficult to deploy in practical scenarios, particularly on edge devices such as mobile and other consumer devices, given their high computational and memory requirements. As a result, there has been a recent interest in the design of quantitative metrics for evaluating deep neural networks that accounts for more than just model accuracy as the sole indicator of network performance. In this study, we continue the conversation towards universal metrics for evaluating the performance of deep neural networks for practical usage. In particular, we propose a new balanced metric called NetScore, which is designed specifically to provide a quantitative assessment of the balance between accuracy, computational complexity, and network architecture complexity of a deep neural network. In what is one of the largest comparative analysis between deep neural networks in literature, the NetScore metric, the top-1 accuracy metric, and the popular information density metric were compared across a diverse set of 50 different deep convolutional neural networks for image classification on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2012) dataset. The evaluation results across these three metrics for this diverse set of networks are presented in this study to act as a reference guide for practitioners in the field. The proposed NetScore metric, along with the other tested metrics, are by no means perfect, but the hope is to push the conversation towards better universal metrics for evaluating deep neural networks for use in practical scenarios to help guide practitioners in model design.
Social Sharing of Emotions on Facebook: Channel Differences, Satisfaction, and Replies

People often share emotions with others in order to manage their emotional experiences. We investigate how social media properties such as visibility and directedness affect how people share emotions in Facebook, and their satisfaction after doing so. 141 participants rated 1,628 of their own recent status updates, posts they made on others' timelines, and private messages they sent for intensity, valence, personal relevance, and overall satisfaction felt after sharing each message. For network-visible channels-status updates and posts on others' timelines-they also rated their satisfaction with replies they received. People shared differently between channels, with more intense and negative emotions in private messages. People felt more satisfied after sharing more positive emotions in all channels and after sharing more personally relevant emotions in network-visible channels. Finally, people's overall satisfaction after sharing emotions in network-visible channels is strongly tied to their reply satisfaction. Quality of replies, not just quantity, matters, suggesting the need for designs that help people receive valuable responses to their shared emotions.
Part orientation and build cost determination in layered manufacturing

As more choices of materials and build processes become available in layered manufacturing (LM), it is increasingly important to identify fundamental problems that underlie the entire field. Determination of best build orientation and minimizing build cost of a part are two such issues that must be considered in any LM process. By decoupling the solution to these problems from a specific LM technology, not only can the solution be applied to a variety of processes, but more realistic cost comparisons of parts built on different machines become possible.
Overview of PAN'16 - New Challenges for Authorship Analysis: Cross-Genre Profiling, Clustering, Diarization, and Obfuscation

This paper presents an overview of the PAN/CLEF evaluation lab. During the last decade, PAN has been established as the main forum of digital text forensic research. PAN 2016 comprises three shared tasks: (i) author identification, addressing author clustering and diarization (or intrinsic plagiarism detection); (ii) author profiling, addressing age and gender prediction from a crossgenre perspective; and (iii) author obfuscation, addressing author masking and obfuscation evaluation. In total, 35 teams participated in all three shared tasks of PAN 2016 and, following the practice of previous editions, software submissions were required and evaluated within the TIRA experimentation framework.
Development of a cross-platform ubiquitous language learning service via mobile phone and interactive television

This paper describes the development processes for a cross-platform ubiquitous language learning service via interactive television (iTV) and mobile phone. Adapting a learner-centred design methodology, a number of requirements were gathered from multiple sources that were subsequently used in TAMALLE (television and mobile phone assisted language learning environment) development.Anumber of issues that arise in the context of cross-platform user interface design and architecture for ubiquitous language learning were tackled. Finally, we discuss a multi-method evaluation regime to gauge usability, perceived usefulness and desirability of TAMALLE system. The result broadly revealed an overall positive response from language learners. Although, there were some reported difficulties in reading text and on-screen display mainly on the iTV side of the interface, TAMALLE was perceived to be a usable, useful and desirable tool to support informal language learning and also for gaining new contextual and cultural knowledge.
Noise2Noise: Learning Image Restoration without Clean Data

We apply basic statistical reasoning to signal reconstruction by machine learning – learning to map corrupted observations to clean signals – with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using clean data, without explicit image priors or likelihood models of the corruption. In practice, we show that a single model learns photographic noise removal, denoising synthetic Monte Carlo images, and reconstruction of undersampled MRI scans – all corrupted by different processes – based on noisy data only.