nils-herrmann/clean_scirepeval huggingface dataset:
A Survey and Classification of Controlled Natural Languages

What is here called controlled natural language (CNL) has traditionally been given many different names. Especially during the last four decades, a wide variety of such languages have been designed. They are applied to improve communication among humans, to improve translation, or to provide natural and intuitive representations for formal notations. Despite the apparent differences, it seems sensible to put all these languages under the same umbrella. To bring order to the variety of languages, a general classification scheme is presented here. A comprehensive survey of existing English-based CNLs is given, listing and describing 100 languages from 1930 until today. Classification of these languages reveals that they form a single scattered cloud filling the conceptual space between natural languages such as English on the one end and formal languages such as propositional logic on the other. The goal of this article is to provide a common terminology and a common model for CNL, to contribute to the understanding of their general nature, to provide a starting point for researchers interested in the area, and to help developers to make design decisions.
Aspect-level opinion mining of online customer reviews

This paper focuses on how to improve aspect-level opinion mining for online customer reviews. We first propose a novel generative topic model, the Joint Aspect/Sentiment (JAS) model, to jointly extract aspects and aspect-dependent sentiment lexicons from online customer reviews. An aspect-dependent sentiment lexicon refers to the aspect-specific opinion words along with their aspect-aware sentiment polarities with respect to a specific aspect. We then apply the extracted aspect-dependent sentiment lexicons to a series of aspect-level opinion mining tasks, including implicit aspect identification, aspect-based extractive opinion summarization, and aspect-level sentiment classification. Experimental results demonstrate the effectiveness of the JAS model in learning aspect- dependent sentiment lexicons and the practical values of the extracted lexicons when applied to these practical tasks.
Cross-Sensor Iris Recognition through Kernel Learning

Due to the increasing popularity of iris biometrics, new sensors are being developed for acquiring iris images and existing ones are being continuously upgraded. Re-enrolling users every time a new sensor is deployed is expensive and time-consuming, especially in applications with a large number of enrolled users. However, recent studies show that cross-sensor matching, where the test samples are verified using data enrolled with a different sensor, often lead to reduced performance. In this paper, we propose a machine learning technique to mitigate the cross-sensor performance degradation by adapting the iris samples from one sensor to another. We first present a novel optimization framework for learning transformations on iris biometrics. We then utilize this framework for sensor adaptation, by reducing the distance between samples of the same class, and increasing it between samples of different classes, irrespective of the sensors acquiring them. Extensive evaluations on iris data from multiple sensors demonstrate that the proposed method leads to improvement in cross-sensor recognition accuracy. Furthermore, since the proposed technique requires minimal changes to the iris recognition pipeline, it can easily be incorporated into existing iris recognition systems.
From centrality to temporary fame: Dynamic centrality in complex networks

We develop a new approach to the study of the dynamics of link utilization in complex networks using records of communication in a large social network. Counter to the perspective that nodes have particular roles, we find roles change dramatically from day to day. “Local hubs” have a power law degree distribution over time, with no characteristic degree value. Our results imply a significant reinterpretation of the concept of node centrality in complex networks, and among other conclusions suggest that interventions targeting hubs will have significantly less effect than previously thought.
To err is human. Building a safer health system

1 National Observatory on Health in the Italian Regions, Università Cattolica, Roma, Italy Dipartimento di Scienze della Salute-Sezione Igiene e Medicina Preventiva, Università di Genova Correspondence to: Cinzia Marano, National Observatory on Health in the Italian Regions, Istituto di Igiene, Università Cattolica del Sacro Cuore, Largo F. Vito,1, 00186 Roma, Italy. E-mail:
Malaria Models with Spatial Effects

Malaria, a vector-borne infectious disease caused by the Plasmodium parasite, is still endemic in more than 100 countries in Africa, Southeast Asia, the Eastern Mediterranean, Western Pacific, Americas, and Europe. In 2010 there were about 219 million malaria cases, with an estimated 660,000 deaths, mostly children under 5 in sub-Saharan Africa (WHO 2012). The malaria parasite is transmitted to humans via the bites of infected female mosquitoes of the genus Anopheles. Mosquitoes can become infected when they feed on the blood of infected humans. Thus the infection goes back and forth between humans and mosquitoes. Mathematical modeling of malaria transmission has a long history. It has helped us to understand transmission mechanism, design and improve control measures, forecast disease outbreaks, etc. The so-called Ross–Macdonald model
MIMO radar: an idea whose time has come

It has recently been shown that multiple-input multiple-output (MIMO) antenna systems have the potential to improve dramatically the performance of communication systems over single antenna systems. Unlike beamforming, which presumes a high correlation between signals either transmitted or received by an array, the MIMO concept exploits the independence between signals at the array elements. In conventional radar, target scintillations are regarded as a nuisance parameter that degrades radar performance. The novelty of MIMO radar is that it takes the opposite view; namely, it capitalizes on target scintillations to improve the radar's performance. We introduce the MIMO concept for radar. The MIMO radar system under consideration consists of a transmit array with widely-spaced elements such that each views a different aspect of the target. The array at the receiver is a conventional array used for direction finding (DF). The system performance analysis is carried out in terms of the Cramer-Rao bound of the mean-square error in estimating the target direction. It is shown that MIMO radar leads to significant performance improvement in DF accuracy.
Ambiguous Intentions: A Paper-like Interface for Creative Design

Interfaces for conceptual and creative design should recognize and interpret drawings. They should also capture users’ intended ambiguity, vagueness, and imprecision and convey these qualities visually and through interactive behavior. Freehand drawing can provide this information and it is a natural input mode for design. We describe a pen-based interface that acquires information about ambiguity and precision from freehand input, represents it internally, and echoes it to users visually and through constraint based
The German Version of the Hopkins Symptoms Checklist-25 (HSCL-25) --factorial structure, psychometric properties, and population-based norms.

The Hopkins Symptom Checklist-25 (HSCL-25) has often been used in cross-cultural settings and in studies focussing on asylum seekers, refugees etc. It is available in a number of languages. The present study investigates the psychometric properties of the German version of the HSCL-25 and delivers population-based norms.



Psychometric properties are investigated in a population-based representative sample of the German general population (N=2516). Seven different factorial models are compared using confirmatory factor analysis.



Two out of the seven models show the best model fit. Because of the high inter-correlations of the factors of the tripartite model, the bifactor model is the preferable factor solution. The internal consistencies (Cronbach's alpha) were 0.84, 0.92, and 0.94 for the anxiety, the depression and the total score, respectively. The correlations of both subscales of this model with the subscales of the Brief-Symptom-Inventory-18 or the Patient Health Questionnaire-4 point out, that there is only marginal differential information of the subscales.



Considering the third ("general") factor of the bifactor model with all items loading on it and the absence of differential correlations of the subscales with the external criteria (PHQ-4, BSI-18) the HSCL-25 seems to assess something like "mental distress" with a focus on symptoms of depression and anxiety. The population-based norms support the application of the HSCL-25 for individual diagnostics as well as for the comparison of specific samples with the general population.
Early-onset hidradenitis suppurativa.

A 9-year-old girl developed hidradenitis suppurativa 3 months after the first signs of adrenarche. Such a close temporal relationship is consistent with the hypothesis that the disease is androgen dependent. Less than 2% of patients have onset of the disease before the age of 11 years. The exceptionally early age of onset in our patient may be partly explained by the fact that she had an early puberty.
A dynamic ID-based remote user authentication scheme

Password-based authentication schemes are the most widely used techniques for remote user authentication. Many static ID-based remote user authentication schemes both with and without smart cards have been proposed. Most of the schemes do not allow the users to choose and change their passwords, and maintain a verifier table to verify the validity of the user login. In this paper we present a dynamic ID-based remote user authentication scheme using smart cards. Our scheme allows the users to choose and change their passwords freely, and do not maintain any verifier table. The scheme is secure against ID-theft, and can resist the reply attacks, forgery attacks, guessing attacks, insider attacks and stolen verifier attacks.
Kernel-based tensor partial least squares for reconstruction of limb movements

We present a new supervised tensor regression method based on multi-way array decompositions and kernel machines. The main issue in the development of a kernel-based framework for tensorial data is that the kernel functions have to be defined on tensor-valued input, which here is defined based on multi-mode product kernels and probabilistic generative models. This strategy enables taking into account the underlying multilinear structure during the learning process. Based on the defined kernels for tensorial data, we develop a kernel-based tensor partial least squares approach for regression. The effectiveness of our method is demonstrated by a real-world application, i.e., the reconstruction of 3D movement trajectories from electrocorticography signals recorded from a monkey brain.
Heuristic evaluation for games: usability principles for video game design

Most video games require constant interaction, so game designers must pay careful attention to usability issues. However, there are few formal methods for evaluating the usability of game interfaces. In this paper, we introduce a new set of heuristics that can be used to carry out usability inspections of video games. The heuristics were developed to help identify usability problems in both early and functional game prototypes. We developed the heuristics by analyzing PC game reviews from a popular gaming website, and the review set covered 108 different games and included 18 from each of 6 major game genres. We analyzed the reviews and identified twelve common classes of usability problems seen in games. We developed ten usability heuristics based on the problem categories, and they describe how common game usability problems can be avoided. A preliminary evaluation of the heuristics suggests that they help identify game-specific usability problems that can easily be overlooked otherwise.
On Trees, Chains and Fast Transactions in the Blockchain

A fundamental open problem in the area of blockchain protocols is whether the Bitcoin protocol is the only solution for building a secure transaction ledger. A recently proposed and widely considered alternative is the GHOST protocol which, notably, was proposed to be at the core of Ethereum as well as other recent proposals for improved Bitcoin-like systems. The GHOST variant is touted as o ering superior performance compared to Bitcoin (potentially o ering block production speed up by a factor of more than 40) without a security loss. Motivated by this, in this work, we study from a provable security point of view the GHOST protocol. We introduce a new formal framework for the analysis of blockchain protocols that relies on trees (rather than chains) and we showcase the power of the framework by providing a uni ed description of the GHOST and Bitcoin protocols, the former of which we extract and formally describe. We then prove that GHOST implements a robust transaction ledger (i.e., possesses liveness and persistence) and hence it is a provably secure alternative to Bitcoin; moreover, our bound for the liveness parameter is superior to that proven for the bitcoin backbone in line with the original expectation for GHOST. Our proof follows a novel methodology for establishing that GHOST is a robust transaction ledger compared to previous works, which may be of independent interest and can be applicable to other blockchain variants.
Microplastics in the marine environment.

This review discusses the mechanisms of generation and potential impacts of microplastics in the ocean environment. Weathering degradation of plastics on the beaches results in their surface embrittlement and microcracking, yielding microparticles that are carried into water by wind or wave action. Unlike inorganic fines present in sea water, microplastics concentrate persistent organic pollutants (POPs) by partition. The relevant distribution coefficients for common POPs are several orders of magnitude in favour of the plastic medium. Consequently, the microparticles laden with high levels of POPs can be ingested by marine biota. Bioavailability and the efficiency of transfer of the ingested POPs across trophic levels are not known and the potential damage posed by these to the marine ecosystem has yet to be quantified and modelled. Given the increasing levels of plastic pollution of the oceans it is important to better understand the impact of microplastics in the ocean food web.
There exists a neural network that does not make avoidable mistakes

The authors show that a multiple-input, single-output, single-hidden-layer feedforward network with (known) hardwired connections from input to hidden layer, monotone squashing at the hidden layer and no squashing at the output embeds as a special case a so-called Fourier network, which yields a Fourier series approximation properties of Fourier series representations. In particular, approximation to any desired accuracy of any square integrable function can be achieved by such a network, using sufficiently many hidden units. In this sense, such networks do not make avoidable mistakes.<<ETX>>
Eliminating Tight Coupling using Subscriptions Subgrouping in Structured Overlays

An advertisement and matching subscription are tightly coupled to activate a single publication routing path in content-based publish/subscribe systems. Due to this tight coupling, instantaneous updates in routing tables are required to generate alternative paths for dynamic routing. This poses serious challenges in offering scalable and robust dynamic routing in cyclic overlays when network conditions like link congestion is detected. We propose, , a distributed publish/subscribe system for content-based dynamic routing in structured cyclic overlays.  uses a novel concept of subscription subgrouping to divide subscriptions into disjoint sets, called subscription subgroups, to eliminate tight coupling. While aiming at deployment in data center networks,  generates routing paths of minimum lengths. We use a homogeneous clustering approach with a bit-vector to realize subscription subgrouping and offer inter-cluster dynamic routing without requiring updates in routing tables. Experiments on a cluster testbed with real world data show that OctopiA reduces the number of saved advertisements in routing tables by 93%, subscription broadcast delay by 33%, static and dynamic publication delivery delays by 25% and 54%, respectively.
A dual-polarization aperture coupled stacked microstrip patch antenna for wideband application

Development and optimization of the aperture-coupled stacked microstrip patch antenna is presented, which has dual polarization with great enhancement of bandwidth. Stacked antenna with aperture feeding mechanism results in great isolation between two feeds. Varying several physical parameters of aperture and matching feed line lengths had very large effect on the performance of the antenna. The microstrip antenna has 42% bandwidth from 7.3 to 11.2 GHz with below 25 dB of isolation between two feeds. The gain was average of 7.5 dBi that highest gain was 10 dBi at feed1 for 9 GHz.
Common mode EMI noise suppression in bridgeless boost PFC converter

Bridgeless boost PFC converter has high efficiency by eliminating the input diode bridge. However, Common Mode (CM) conducted EMI becomes a great issue. The goal of this paper is to study the possibility to minimize the CM noise in this converter. First the noise model is studied. Then a balance concept is introduced and applied to cancel the CM noise. Two approaches to minimize CM noise are introduced and compared. Experiments verify the effectiveness of both approaches.
Analysis of Half-Quadratic Minimization Methods for Signal and Image Recovery

We address the minimization of regularized convex cost functions which are customarily used for edge-preserving restoration and reconstruction of signals and images. In order to accelerate computation, the multiplicative and the additive half-quadratic reformulation of the original cost-function have been pioneered in Geman and Reynolds [ Machine Intelligence, 14 (1992), pp. 367–383] and Geman and Yang [ (1995), pp. 932–946]. The alternate minimization of the resultant (augmented) cost-functions has a simple explicit form. The goal of this paper is to provide a systematic analysis of the convergence rate achieved by these methods. For the multiplicative and additive half-quadratic regularizations, we determine their upper bounds for their root-convergence factors. The bound for the multiplicative form is seen to be always smaller than the bound for the additive form. Experiments show that the number of iterations required for convergence for the multiplicative form is always less than that for the additive form. However, the computational cost of each iteration is much higher for the multiplicative form than for the additive form. The global assessment is that minimization using the additive form of half-quadratic regularization is faster than using the multiplicative form. When the additive form is applicable, it is hence recommended. Extensive experiments demonstrate that in our MATLAB implementation, both methods are substantially faster (in terms of computational times) than the standard MATLAB Optimization Toolbox routines used in our comparison study.
The genomic landscapes of human breast and colorectal cancers.

Human cancer is caused by the accumulation of mutations in oncogenes and tumor suppressor genes. To catalog the genetic changes that occur during tumorigenesis, we isolated DNA from 11 breast and 11 colorectal tumors and determined the sequences of the genes in the Reference Sequence database in these samples. Based on analysis of exons representing 20,857 transcripts from 18,191 genes, we conclude that the genomic landscapes of breast and colorectal cancers are composed of a handful of commonly mutated gene "mountains" and a much larger number of gene "hills" that are mutated at low frequency. We describe statistical and bioinformatic tools that may help identify mutations with a role in tumorigenesis. These results have implications for understanding the nature and heterogeneity of human cancers and for using personal genomics for tumor diagnosis and therapy.
A Novel Boil-Off Gas Re-Liquefaction Using a Spray Recondenser for Liquefied Natural-Gas Bunkering Operations

This study presents the design of a novel boil-off gas (BOG) re-liquefaction technology using a BOG recondenser system. The BOG recondenser system targets the liquefied natural gas (LNG) bunkering operation, in which the BOG phase transition occurs in a pressure vessel instead of a heat exchanger. The BOG that is generated during LNG bunkering operation is characterized as an intermittent flow with various peak loads. The system was designed to temporarily store the transient BOG inflow, condense it with subcooled LNG and store the condensed liquid. The superiority of the system was verified by comparing it with the most extensively employed conventional re-liquefaction system in terms of consumption energy and via an exergy analysis. Static simulations were conducted for three compositions; the results indicated that the proposed system provided 0 to 6.9% higher efficiencies. The exergy analysis indicates that the useful work of the conventional system is 24.9%, and the useful work of the proposed system is 26.0%. Process dynamic simulations of six cases were also performed to verify the behaviour of the BOG recondenser system. The results show that the pressure of the holdup in the recondenser vessel increased during the BOG inflow mode and decreased during the initialization mode. The maximum pressure of one of the bunkering cases was 3.45 bar. The system encountered a challenge during repetitive operations due to overpressurizing of the BOG recondenser vessel.
Active learning from noisy and abstention feedback

An active learner is given an instance space, a label space and a hypothesis class, where one of the hypotheses in the class assigns ground truth labels to instances. Additionally, the learner has access to a labeling oracle, which it can interactively query for the label of any example in the instance space. The goal of the learner is to find a good estimate of the hypothesis in the hypothesis class that generates the ground truth labels while making as few interactive queries to the oracle as possible. This work considers a more general setting where the labeling oracle can abstain from providing a label in addition to returning noisy labels. We provide a model for this setting where the abstention rate and the noise rate increase as we get closer to the decision boundary of the ground truth hypothesis. We provide an algorithm and an analysis of the number of queries it makes to the labeling oracle; finally we provide matching lower bounds to demonstrate that our algorithm has near-optimal estimation accuracy.
On cloud security attacks: A taxonomy and intrusion detection and prevention as a service

Major provisioning of cloud computing is mainly delivered via Software as a Service, Platform as a Service and Infrastructure as a Service. However, these service delivery models are vulnerable to a range of security attacks, exploiting both cloud specific and existing web service vulnerabilities. Taxonomies are a useful tool for system designers as they provide a systematic way of understanding, identifying and addressing security risks. In this research work, Cloud based attacks and vulnerabilities are collected and classify with respect to their cloud models. We also present taxonomy of cloud security attacks and potential mitigation strategies with the aim of providing an in-depth understanding of security requirements in the cloud environment. We also highlight the importance of intrusion detection and prevention as a service. Cloud computing, Taxonomy, Security attacks, Intrusion detection