nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Linguistic Annotation of Large Scale L 2 Databases : The EF-Cambridge Open Language Database ( EFCamDat )

∗Naturalistic learner productions are an important empirical resource for SLA research. Some pioneering works have produced valuable second language (L2) resources supporting SLA  One common limitation of these resources is the absence of individual longitudinal data for numerous speakers with different backgrounds across the proficiency spectrum, which is vital for understanding the nature of individual variation in longitudinal development.2 A second limitation is the relatively restricted amounts of data annotated with linguistic information (e.g., lexical, morphosyntactic, semantic features, etc.) to support investigation of SLA hypotheses and obtain patterns of development for different linguistic phenomena. Where available, annotations tend to be manually obtained, a situation posing immediate limitations to the quantity of data that could be annotated with reasonable human resources and within reasonable time. Natural Language Processing (NLP) tools can provide automatic annotations for parts-of-speech (POS) and syntactic structure and are indeed increasingly applied to learner language in various contexts. Systems in computer-assisted language learning (CALL) have used a parser and other NLP tools to automatically detect learner errors and provide feedback  Some work aimed at adapting annotations provided by parsing tools to accurately describe learner syntax (Dickinson & Lee, 2009) or evaluated parser performance on learner language and the effect of learner errors on the parser. Krivanek and Meurers (2011) compared two parsing methods, one using a hand-crafted lexicon and one trained on a corpus. They found that the former is more successful in recovering the main grammatical dependency relations whereas the latter is more successful in recovering optional, adjunction relations. Ott and Ziai (2010) evaluated the performance of a dependency parser trained on native German (MaltParser; Nivre et al., 2007) on 106 learner answers to a comprehension task in L2 German. Their study indicates that while some errors can be problematic for the parser (e.g., omission of finite verbs) many others (e.g., wrong word order) can be parsed robustly, resulting in overall high performance scores. In this paper we have two goals. First, we introduce a new English L2 database, the EF Cambridge Open Language Database, henceforth EFCAMDAT. EFCAMDAT was developed by the Department of Theoretical and Applied Linguistics at the University of Cambridge in collaboration with EF Education First, an international educational organization. It contains writings submitted to Englishtown, the
Procedural City Layout Generation Based on Urban Land Use Models

Training and simulation applications in virtual worlds require significant amounts of urban environments. Procedural generation is an efficient way to create such models. Existing approaches for procedural modelling of cities aim at facilitating the work of urban planners and artists, but either require expert knowledge or external input data to generate results that resemble real-life cities, or they have long computation times, and thus are unsuitable for non-experts such as training instructors. We propose a method that procedurally creates layouts of structurally plausible cities from high-level, intuitive user input such as city size, location and historic background. The resulting layouts consist of different kinds of city districts which are arranged using constraints derived from established models of urban land use. Our approach avoids the need for external expert engagement in the creation process, and allows for the generation of large city layouts in seconds, making it significantly faster than comparable agent-based software and thus supporting the needs of non-expert creators of virtual cities for many applications.
Online Metric Learning and Fast Similarity Search

Metric learning algorithms can provide useful distance functions for a variety of domains, and recent work has shown good accuracy for problems where the learner can access all distance constraints at once. However, in many real applications, constraints are only available incrementally, thus necessitating methods that can perform online updates to the learned metric. Existing online algorithms offer bounds on worst-case performance, but typically do not perform well in practice as compared to their offline counterparts. We present a new online metric learning algorithm that updates a learned Mahalanobis metric based on LogDet regularization and gradient descent. We prove theoretical worst-case performance bounds, and empirically compare the proposed method against existing online metric learning algorithms. To further boost the practicality of our approach, we develop an online locality-sensitive hashing scheme which leads to efficient updates to data structures used for fast approximate similarity search. We demonstrate our algorithm on multiple datasets and show that it outperforms relevant baselines.
Wireless Mesh Software Defined Networks (wmSDN)

In this paper we propose to integrate Software Defined Networking (SDN) principles in Wireless Mesh Networks (WMN) formed by OpenFlow switches. The use of a centralized network controller and the ability to setup arbitrary paths for data flows make SDN a handy tool to deploy fine-grained traffic engineering algorithms in WMNs. However, centralized control may be harmful in multi-hop radio networks formed by commodity devices (e.g. Wireless Community Networks), in which node isolation and network fragmentation are not rare events. To exploit the pros and mitigate the cons, our framework uses the traditional OpenFlow centralized controller to engineer the routing of data traffic, while it uses a distributed controller based on OLSR to route: i) OpenFlow control traffic, ii) data traffic, in case of central controller failure. We implemented and tested our Wireless Mesh Software Defined Network (wmSDN) showing its applicability to a traffic engineering use-case, in which the controller logic balances outgoing traffic among the Internet gateways of the mesh. Albeit simple, this use case allows showing a possible usage of SDN that improves user performance with respect to the case of a traditional mesh with IP forwarding and OLSR routing. The wmSDN software toolkit is formed by Open vSwitch, POX controller, OLSR daemon and our own Bash and Python scripts. The tests have been carried out in an emulation environment based on Linux Containers, NS3 and CORE tools.
Structure, culture and Simmelian ties in entrepreneurial firms

This article develops a cultural agreement approach to organizational culture that emphasizes how clusters of individuals reinforce potentially idiosyncratic understandings of many aspects of culture including the structure of network relations. Building on recent work concerning Simmelian tied dyads (defined as dyads embedded in three-person cliques), the research examines perceptions concerning advice and friendship relations in three entrepreneurial firms. The results support the idea that Simmelian tied dyads (relative to dyads in general) reach higher agreement concerning who is tied to whom, and who are embedded together in triads in organizations.
Wasserstein GAN

The problem this paper is concerned with is that of unsupervised learning. Mainly, what does it mean to learn a probability distribution? The classical answer to this is to learn a probability density. This is often done by defining a parametric family of densities (Pθ)θ∈Rd and finding the one that maximized the likelihood on our data: if we have real data examples {x}i=1, we would solve the problem
The evolution of an orbiting sample container for potential Mars sample return

Although NASA has no specific plans at this time to return samples from Mars, the Program Formulation Office of the Mars Exploration Program sponsors ongoing mission concept studies, systems analyses, and technology investments which explore different strategies for the potential return of samples from Mars, consistent with the charter of the program and stated priorities of the science community. A critical component of such a campaign would be an Orbiting Sample container (OS), which would contain the Mars samples to be returned to Earth. This paper discusses the most recent efforts by the JPL's Mars Formulation Office to mature an OS design planned for use on a potential Mars Sample Return (MSR) mission. Similar to the “Decadal Study Architecture” , the current MSR architecture envisions as a three-mission campaign with each mission serving a critical role towards returning Martian rock and atmospheric samples back to Earth. An OS would be a central piece of hardware in the proposed MSR architecture due to its interfaces to all the three missions of the potential campaign. Additionally, numerous stakeholders and subsystems such as science and planetary protection impose challenging requirements on the OS's functions and capabilities. As a result, designing an OS that meets all the requirements is challenging and quite complex. The story of the OS's evolution from black box concept thru to the current-and-still-maturing baseline design is the focus of this paper. From the OS's launch off Earth aboard a Sample Retrieval Lander (SRL) through to return to Earth, the design and functional requirements generated by and for each stage of the OS's mission are discussed. Then, with an understanding of what the OS would be required to do, a mapping of the main requirements to the design features of the current OS concept is explained. Many tests and analyses have been conducted to support and validate the current OS design. Results from test and analysis in the areas of aerothermal, impact dynamics, optical tracking, and radio electromagnetics are presented.
FSX: fine-grained incremental unit test generation for C/C++ programs

Automated unit test generation bears the promise of significantly reducing test cost and hence improving software quality. However, the maintenance cost of the automatically generated tests presents a significant barrier to adoption of this technology. To address this challenge, we propose a novel technique for automated and fine-grained incremental generation of unit tests through minimal augmentation of an existing test suite. The technique uses iterative, incremental refinement of test-drivers and symbolic execution, guided by a diagnostics engine. The diagnostics engine works off a novel precise and efficient byte-level dynamic dependence analysis built using Reduced Ordered Binary Decision Diagrams (ROBDDs). We present a tool FSX implementing this technique and evaluate it under two practical use-cases of incremental unit test generation, on five revisions of the open-source software iPerf, as well as on 3 large subjects, comprising more than 60 thousand lines of code, from in-house commercial network products. The evaluation shows that FSX can generate high-quality unit tests on large industrial software while minimizing the maintenance cost of the overall test-suite.
The Earth Mover's Distance as a Metric for Image Retrieval

We investigate the properties of a metric between two distributions, the Earth Mover's Distance (EMD), for content-based image retrieval. The EMD is based on the minimal cost that must be paid to transform one distribution into the other, in a precise sense, and was first proposed for certain vision problems by Peleg, Werman, and Rom. For image retrieval, we combine this idea with a representation scheme for distributions that is based on vector quantization. This combination leads to an image comparison framework that often accounts for perceptual similarity better than other previously proposed methods. The EMD is based on a solution to the transportation problem from linear optimization, for which efficient algorithms are available, and also allows naturally for partial matching. It is more robust than histogram matching techniques, in that it can operate on variable-length representations of the distributions that avoid quantization and other binning problems typical of histograms. When used to compare distributions with the same overall mass, the EMD is a true metric. In this paper we focus on applications to color and texture, and we compare the retrieval performance of the EMD with that of other distances.
A Low-Profile Dual-Band Dual-Polarized Antenna With an AMC Surface for WLAN Applications

In this letter, a novel low-profile dual-band dual-polarized antenna with an artificial magnetic conductor (AMC) surface for wireless local area network (WLAN) applications is presented. Two sets of bowtie patches are introduced to form two dipoles in ±45° polarizations. Two integrated baluns are used to excite the dipoles. By inserting an AMC surface, the antenna can achieve unidirectional radiation pattern and low-profile characteristics. The height of the proposed antenna is 0.088λ0 at 2.4 GHz. It can provide measured 15.6% (2.36–2.76 GHz) and 9.3% (5.12–5.62 GHz) relative bandwidth, respectively. Port-to-port isolation higher than 22 dB can be realized. Stable radiation patterns with peak gain of 7.2 dBi in lower band and 7.3 dBi in upper band are also obtained. The proposed antenna can be used for multiband base stations for WLAN applications.
Search , Obfuscation , and Price Elasticities on the Internet 1

We examine the competition between a group of Internet retailers who operate in an environment where a price search engine plays a dominant role. We show that for some products in this environment, the easy price search makes demand tremendously pricesensitive. Retailers, though, engage in obfuscation—practices that frustrate consumer search or make it less damaging to firms—resulting in much less price sensitivity on some other products. We discuss several models of obfuscation and examine its effects on demand and markups empirically.
Synthesising isiZulu-English Code-Switch Bigrams Using Word Embeddings

Code-switching is prevalent among South African speakers, and presents a challenge to automatic speech recognition systems. It is predominantly a spoken phenomenon, and generally does not occur in textual form. Therefore a particularly serious challenge is the extreme lack of training material for language modelling. We investigate the use of word embeddings to synthesise isiZulu-to-English code-switch bigrams with which to augment such sparse language model training data. A variety of word embeddings are trained on a monolingual English web text corpus, and subsequently queried to synthesise code-switch bigrams. Our evaluation is performed on language models trained on a new, although small, English-isiZulu codeswitch corpus compiled from South African soap operas. This data is characterised by fast, spontaneously spoken speech containing frequent code-switching. We show that the augmentation of the training data with code-switched bigrams synthesised in this way leads to a reduction in perplexity.
Designing Password Policies for Strength and Usability

Password-composition policies are the result of service providers becoming increasingly concerned about the security of online accounts. These policies restrict the space of user-created passwords to preclude easily guessed passwords and thus make passwords more difficult for attackers to guess. However, many users struggle to create and recall their passwords under strict password-composition policies, for example, ones that require passwords to have at least eight characters with multiple character classes and a dictionary check. Recent research showed that a promising alternative was to focus policy requirements on password length instead of on complexity. In this work, we examine 15 password policies, many focusing on length requirements. In doing so, we contribute the first thorough examination of policies requiring longer passwords. We conducted two online studies with over 20,000 participants, and collected both usability and password-strength data. Our findings indicate that password strength and password usability are not necessarily inversely correlated: policies that lead to stronger passwords do not always reduce usability. We identify policies that are both more usable and more secure than commonly used policies that emphasize complexity rather than length requirements. We also provide practical recommendations for service providers who want their users to have strong yet usable passwords.
Fast Nonnegative Matrix/Tensor Factorization Based on Low-Rank Approximation

Nonnegative matrix factorization (NMF) algorithms often suffer from slow convergence speed due to the nonnegativity constraints, especially for large-scale problems. Low-rank approximation methods such as principle component analysis (PCA) are widely used in matrix factorizations to suppress noise, reduce computational complexity and memory requirements. However, they cannot be applied to NMF directly so far as they result in factors with mixed signs. In this paper, low-rank approximation is introduced to NMF (named lraNMF), which is not only able to reduce the computational complexity of NMF algorithms significantly, but also suppress bipolar noise. In fact, the new update rules are typically about times faster than traditional ones of NMF, here is the number of observations and is the low rank of latent factors. Therefore lraNMF is particularly efficient in the case where , which is the general case in NMF. The proposed update rules can also be incorporated into most existing NMF algorithms straightforwardly as long as they are based on Euclidean distance. Then the concept of lraNMF is generalized to the tensor field to perform a fast sequential nonnegative Tucker decomposition (NTD). By applying the proposed methods, the practicability of NMF/NTD is significantly improved. Simulations on synthetic and real data show the validity and efficiency of the proposed approaches.
Experience with molluscum contagiosum and associated inflammatory reactions in a pediatric dermatology practice: the bump that rashes.

To investigate the frequency, epidemiology, clinical features, and prognostic significance of inflamed molluscum contagiosum (MC) lesions, molluscum dermatitis, reactive papular eruptions resembling Gianotti-Crosti syndrome, and atopic dermatitis in patients with MC.  Retrospective medical chart review.  University-based pediatric dermatology practice.  A total of 696 patients (mean age, 5.5 years) with molluscum.  Frequencies, characteristics, and associated features of inflammatory reactions to MC in patients with and without atopic dermatitis.  Molluscum dermatitis, inflamed MC lesions, and Gianotti-Crosti syndrome-like reactions (GCLRs) occurred in 270 (38.8%), 155 (22.3%), and 34 (4.9%) of the patients, respectively. A total of 259 patients (37.2%) had a history of atopic dermatitis. Individuals with atopic dermatitis had higher numbers of MC lesions (P < .001) and an increased likelihood of molluscum dermatitis (50.6% vs 31.8%; P < .001). In patients with molluscum dermatitis, numbers of MC lesions increased during the next 3 months in 23.4% of those treated with a topical corticosteroid and 33.3% of those not treated with a topical corticosteroid, compared with 16.8% of patients without dermatitis. Patients with inflamed MC lesions were less likely to have an increased number of MC lesions over the next 3 months than patients without inflamed MC lesions or dermatitis (5.2% vs 18.4%; P < .03). The GCLRs were associated with inflamed MC lesion (P < .001), favored the elbows and knees, tended to be pruritic, and often heralded resolution of MC. Two patients developed unilateral laterothoracic exanthem-like eruptions.  Inflammatory reactions to MC, including the previously underrecognized GCLR, are common. Treatment of molluscum dermatitis can reduce spread of MC via autoinoculation from scratching, whereas inflamed MC lesions and GCLRs reflect cell-mediated immune responses that may lead to viral clearance.
Acceptance Model in the Domains of LIS and Education : A Review of Selected Literature

The Technology Acceptance Model (TAM) is a theoretical framework that is most extensively utilized in explaining an individual’s acceptance of an information technology or an information system. This study reviews numerous literature related to the TAM focusing on TAM applications in the domains of Library and Information Science (LIS) and Education. The different studies in these areas were evaluated to understand the modifications incorporated into this model. The study attempts provide insight on future trends in the technology acceptance model as well to help identify gaps in literature where future research could be conducted.
Preliminary, open-label, pilot study of add-on oral Δ9-tetrahydrocannabinol in chronic post-traumatic stress disorder.

Many patients with post-traumatic stress disorder (PTSD) achieve but partial remission with current treatments. Patients with unremitted PTSD show high rates of substance abuse. Marijuana is often used as compassion add-on therapy for treatment-resistant PTSD. This open-label study evaluates the tolerance and safety of orally absorbable Δ(9)-tetrahydrocannabinol (THC) for chronic PTSD.



Ten outpatients with chronic PTSD, on stable medication, received 5 mg of Δ(9)-THC twice a day as add-on treatment.



There were mild adverse effects in three patients, none of which led to treatment discontinuation. The intervention caused a statistically significant improvement in global symptom severity, sleep quality, frequency of nightmares, and PTSD hyperarousal symptoms.



Orally absorbable Δ(9)-THC was safe and well tolerated by patients with chronic PTSD.
Efficient Name Disambiguation for Large-Scale Databases

Name disambiguation can occur when one is seeking a list of publications of an author who has used different name variations and when there are multiple other authors with the same name. We present an efficient integrative framework for solving the name disambiguation problem: a blocking method retrieves candidate classes of authors with similar names and a clustering method, DBSCAN, clusters papers by author. The distance metric between papers used in DBSCAN is calculated by an online active selection support vector machine algorithm (LASVM), yielding a simpler model, lower test errors and faster prediction time than a standard SVM. We prove that by recasting transitivity as density reachability in DBSCAN, transitivity is guaranteed for core points. For evaluation, we manually annotated 3,355 papers yielding 490 authors and achieved 90.6% pairwise-F1. For scalability, authors in the entire CiteSeer dataset, over 700,000 papers, were readily disambiguated.
Scan Matching for Graph SLAM in Indoor Dynamic Scenarios

SLAM (Simultaneous Localization And Mapping) plays an essential and important role for mobile robotic autonomous navigation. SLAM in dynamic environments with moving objects is a challenging problem. We focus on scan matching for Graph-SLAM in indoor dynamic scenarios. Scan matching algorithm is proposed and implemented, which consists of the following phases: first, conditioned Hough Transform based segmentation is performed to extract and group line features; second, occupancy-analysis based moving objects detection is done to detect and discard the segments corresponding to the moving objects; third, linear regression based line feature matching is executed to estimate the roto-translation parameters. Simulations to estimate roto-translation and the entire trajectory of the robot effectively verified the robustness of this algorithm in a dynamic scenario. The proposed algorithm is based on the line features of the indoor environment. It is robust to disturbances from moving objects in the dynamic scenario, and is especially suitable for the case when large rotational displacement is present.