nils-herrmann/clean_scirepeval huggingface dataset:
Unmanned aircraft systems in maritime operations: Challenges addressed in the scope of the SEAGULL project

The SEAGULL project aims at the development of intelligent systems to support maritime situation awareness based on unmanned aerial vehicles. It proposes to create an intelligent maritime surveillance system by equipping unmanned aerial vehicles (UAVs) with different types of optical sensors. Optical sensors such as cameras (visible, infrared, multi and hyper spectral) can contribute significantly to the generation of situational awareness of maritime events such as (i) detection and georeferencing of oil spills or hazardous and noxious substances; (ii) tracking systems (e.g. vessels, shipwrecked, lifeboat, debris, etc.); (iii) recognizing behavioral patterns (e.g. vessels rendezvous, high-speed vessels, atypical patterns of navigation, etc.); and (iv) monitoring parameters and indicators of good environmental status. On-board transponders will be used for collision detection and avoidance mechanism (sense and avoid). This paper describes the core of the research and development work done during the first 2 years of the project with particular emphasis on the following topics: system architecture, automatic detection of sea vessels by vision sensors and custom designed computer vision algorithms; and a sense and avoid system developed in the theoretical framework of zero-sum differential games.
The mask detection technology for occluded face analysis in the surveillance system.

The surveillance systems have been widely used in automatic teller machines (ATMs), banks, convenient stores, etc. For example, when a customer uses the ATM, the surveillance systems will record his/her face information. The information will help us understand and trace who withdrew money. However, when criminals use the ATM to withdraw illegal money, they usually block their faces with something (in Taiwan, criminals usually use safety helmets or masks to block their faces). That will degrade the purpose of the surveillance system. In previous work, we already proposed a technology for safety helmet detection. In this paper, we propose a mask detection technology based upon automatic face recognition methods. We use the Gabor filters to generate facial features and utilize geometric analysis algorithms for mask detection. The technology can give an early warning to save-guards when any "customer" or "intruder" blocks his/her face information with a mask. Besides, the technology can assist face detection in the automatic face recognition system. Experimental results show the performance and reliability of the proposed technology.
Spark-based anomaly detection over multi-source VMware performance data in real-time

Anomaly detection refers to identifying the patterns in data that deviate from expected behavior. These non-conforming patterns are often termed as outliers, malwares, anomalies or exceptions in different application domains. This paper presents a novel, generic real-time distributed anomaly detection framework for multi-source stream data. As a case study, we have decided to detect anomaly for multi-source VMware-based cloud data center. The framework monitors VMware performance stream data (e.g., CPU load, memory usage, etc.) continuously. It collects these data simultaneously from all the VMwares connected to the network. It notifies the resource manager to reschedule its resources dynamically when it identifies any abnormal behavior of its collected data. We have used Apache Spark, a distributed framework for processing performance stream data and making prediction without any delay. Spark is chosen over a traditional distributed framework (e.g., Hadoop and MapReduce, Mahout, etc.) that is not ideal for stream data processing. We have implemented a flat incremental clustering algorithm to model the benign characteristics in our distributed Spark based framework. We have compared the average processing latency of a tuple during clustering and prediction in Spark with Storm, another distributed framework for stream data processing. We experimentally find that Spark processes a tuple much quicker than Storm on average.
Green synthesis and characterization of silver nanoparticles using Artemisia absinthium aqueous extract--A comprehensive study.

Unlike chemical synthesis, biological synthesis of nanoparticles is gaining tremendous interest, and plant extracts are preferred over other biological sources due to their ample availability and wide array of reducing metabolites. In this project, we investigated the reducing potential of aqueous extract of Artemisia absinthium L. for synthesizing silver nanoparticles (AgNPs). Optimal synthesis of AgNPs with desirable physical and biological properties was investigated using ultra violet-visible spectroscopy (UV-vis), dynamic light scattering (DLS), transmission electron microscopy (TEM) and energy-dispersive X-ray analysis (EDX). To determine their appropriate concentrations for AgNP synthesis, two-fold dilutions of silver nitrate (20 to 0.62 mM) and aqueous plant extract (100 to 0.79 mg ml(-1)) were reacted. The results showed that silver nitrate (2mM) and plant extract (10 mg ml(-1)) mixed in different ratios significantly affected size, stability and yield of AgNPs. Extract to AgNO3 ratio of 6:4v/v resulted in the highest conversion efficiency of AgNO3 to AgNPs, with the particles in average size range of less than 100 nm. Furthermore, the direct imaging of synthesized AgNPs by TEM revealed polydispersed particles in the size range of 5 to 20 nm. Similarly, nanoparticles with the characteristic peak of silver were observed with EDX. This study presents a comprehensive investigation of the differential behavior of plant extract and AgNO3 to synthesize biologically stable AgNPs.
Green spaces and cognitive development in primary schoolchildren.

Exposure to green space has been associated with better physical and mental health. Although this exposure could also influence cognitive development in children, available epidemiological evidence on such an impact is scarce. This study aimed to assess the association between exposure to green space and measures of cognitive development in primary schoolchildren. This study was based on 2,593 schoolchildren in the second to fourth grades (7-10 y) of 36 primary schools in Barcelona, Spain (2012-2013). Cognitive development was assessed as 12-mo change in developmental trajectory of working memory, superior working memory, and inattentiveness by using four repeated (every 3 mo) computerized cognitive tests for each outcome. We assessed exposure to green space by characterizing outdoor surrounding greenness at home and school and during commuting by using high-resolution (5 m × 5 m) satellite data on greenness (normalized difference vegetation index). Multilevel modeling was used to estimate the associations between green spaces and cognitive development. We observed an enhanced 12-mo progress in working memory and superior working memory and a greater 12-mo reduction in inattentiveness associated with greenness within and surrounding school boundaries and with total surrounding greenness index (including greenness surrounding home, commuting route, and school). Adding a traffic-related air pollutant (elemental carbon) to models explained 20-65% of our estimated associations between school greenness and 12-mo cognitive development. Our study showed a beneficial association between exposure to green space and cognitive development among schoolchildren that was partly mediated by reduction in exposure to air pollution.
Heterogeneous Integration for Mid-infrared Silicon Photonics

Heterogeneous integration enables the construction of silicon (Si) photonic systems, which are fully integrated with a range of passive and active elements including lasers and detectors. Numerous advancements in recent years have shown that heterogeneous Si platforms can be extended beyond near-infrared telecommunication wavelengths to the mid-infrared (MIR) (2–20 μm) regime. These wavelengths hold potential for an extensive range of sensing applications and the necessary components for fully integrated heterogeneous MIR Si photonic technologies have now been demonstrated. However, due to the broad wavelength range and the diverse assortment of MIR technologies, the optimal platform for each specific application is unclear. Here, we overview Si photonic waveguide platforms and lasers at the MIR, including quantum cascade lasers on Si. We also discuss progress toward building an integrated multispectral source, which can be constructed by wavelength beam combining the outputs from multiple lasers with arrayed waveguide gratings and duplexing adiabatic couplers.
Mental health literacy measures evaluating knowledge, attitudes and help-seeking: a scoping review.

Mental health literacy has received increasing attention as a useful strategy to promote early identification of mental disorders, reduce stigma and enhance help-seeking behaviors. However, despite the abundance of research on mental health literacy interventions, there is the absence of evaluations of current available mental health literacy measures and related psychometrics. We conducted a scoping review to bridge the gap.



We searched PubMed, PsycINFO, Embase, CINAHL, Cochrane Library, and ERIC for relevant studies. We only focused on quantitative studies and English publications, however, we didn't limit study participants, locations, or publication dates. We excluded non-English studies, and did not check the grey literature (non peer-reviewed publications or documents of any type) and therefore may have missed some eligible measures.



We located 401 studies that include 69 knowledge measures (14 validated), 111 stigma measures (65 validated), and 35 help-seeking related measures (10 validated). Knowledge measures mainly investigated the ability of illness identification, and factual knowledge of mental disorders such as terminology, etiology, diagnosis, prognosis, and consequences. Stigma measures include those focused on stigma against mental illness or the mentally ill; self-stigma ; experienced stigma; and stigma against mental health treatment and help-seeking. Help-seeking measures included those of help-seeking attitudes, intentions to seek help, and actual help-seeking behaviors.



Our review provides a compendium of available mental health literacy measures to facilitate applying existing measures or developing new measures. It also provides a solid database for future research on systematically assessing the quality of the included measures.
Metabolism and functions of glutathione in brain

The tripeptide glutathione is the thiol compound present in the highest concentration in cells of all organs. Glutathione has many physiological functions including its involvement in the defense against reactive oxygen species. The cells of the human brain consume about 20% of the oxygen utilized by the body but constitute only 2% of the body weight. Consequently, reactive oxygen species which are continuously generated during oxidative metabolism will be generated in high rates within the brain. Therefore, the detoxification of reactive oxygen species is an essential task within the brain and the involvement of the antioxidant glutathione in such processes is very important. The main focus of this review article will be recent results on glutathione metabolism of different brain cell types in culture. The glutathione content of brain cells depends strongly on the availability of precursors for glutathione. Different types of brain cells prefer different extracellular glutathione precursors. Glutathione is involved in the disposal of peroxides by brain cells and in the protection against reactive oxygen species. In coculture astroglial cells protect other neural cell types against the toxicity of various compounds. One mechanism for this interaction is the supply by astroglial cells of glutathione precursors to neighboring cells. Recent results confirm the prominent role of astrocytes in glutathione metabolism and the defense against reactive oxygen species in brain. These results also suggest an involvement of a compromised astroglial glutathione system in the oxidative stress reported for neurological disorders.
Lean Reachability Tree for Unbounded Petri Nets

Elaborate efforts have been made to eliminate fake markings and refine <inline-formula> <tex-math notation="LaTeX">${\omega }$ </tex-math></inline-formula>-markings in the existing modified or improved Karp–Miller trees for various classes of unbounded Petri nets since the late 1980s. The main issues fundamentally are incurred due to the generation manners of the trees that prematurely introduce some potentially unbounded markings with <inline-formula> <tex-math notation="LaTeX">${\omega }$ </tex-math></inline-formula> symbols and keep their growth into new ones. Aiming at addressing them, this work presents a non-Karp–Miller tree called a lean reachability tree (LRT). First, a sufficient and necessary condition of the unbounded places and some reachability properties are established to reveal the features of unbounded nets. Then, we present an LRT generation algorithm with a sufficiently enabling condition (SEC). When generating a tree, SEC requires that the components of a covering node are not replaced by <inline-formula> <tex-math notation="LaTeX">${\omega }$ </tex-math></inline-formula> symbols, but continue to grow until any transition on an output path of an unbounded place has been branch-enabled at least once. In return, no fake marking is produced and no legal marking is lost during the tree generation. We prove that LRT can faithfully express by folding, instead of equivalently representing, the reachability set of an unbounded net. Also, some properties of LRT are examined and a sufficient condition of deadlock existence based on it is given. The case studies show that LRT outperforms the latest modified Karp–Miller trees in terms of size, expressiveness, and applicability. It can be applied to the analysis of the emerging discrete event systems with infinite states.
Handcrafted vs. non-handcrafted features for computer vision classification

This work presents a generic computer vision system designed for exploiting trained deep Convolutional Neural Networks (CNN) as a generic feature extractor and mixing these features with more traditional hand-crafted features. Such a system is a single structure that can be used for synthesizing a large number of different image classification tasks. Three substructures are proposed for creating the generic computer vision system starting from handcrafted and non-handcrafter features: i) one that remaps the output layer of a trained CNN to classify a different problem using an SVM; ii) a second for exploiting the output of the penultimate layer of a trained CNN as a feature vector to feed an SVM; and iii) a third for merging the output of some deep layers, applying a dimensionality reduction method, and using these features as the input to an SVM. The application of feature transform techniques to reduce the dimensionality of feature sets coming from the deep layers represents one of the main contributions of this paper. Three approaches are used for the non-handcrafted features: deep
Implementing Gender-Dependent Vowel-Level Analysis for Boosting Speech-Based Depression Recognition

Whilst studies on emotion recognition show that genderdependent analysis can improve emotion classification performance, the potential differences in the manifestation of depression between male and female speech have yet to be fully explored. This paper presents a qualitative analysis of phonetically aligned acoustic features to highlight differences in the manifestation of depression. Gender-dependent analysis with phonetically aligned gender-dependent features are used for speech-based depression recognition. The presented experimental study reveals gender differences in the effect of depression on vowel-level features. Considering the experimental study, we also show that a small set of knowledge-driven gender-dependent vowel-level features can outperform state-of-the-art turn-level acoustic features when performing a binary depressed speech recognition task. A combination of these preselected gender-dependent vowel-level features with turn-level standardised openSMILE features results in additional improvement for depression recognition.
RFID Transponders

The objective of this article is to review radio frequency identification (RFID) technology, its developments on RFID transponders, design and operating principles, so that end users can benefit from knowing which transponder meets their requirements. In this article, RFID system definition, RFID transponder architecture and RFID transponder classification based on a comprehensive literature review on the field of research are presented. Detailed descriptions of these tags are also presented, as well as an in-house developed semiactive tag in a compact package.
A high-rising epiglottis: a benign anatomical variant.

We report an asymptomatic 10-year-old boy who was found to have a high-rising epiglottis visible in his pharynx. This benign anatomical variant is not widely recognized yet may cause anxiety to patients and their families. The prevalence of this finding is controversial, and it is uncertain whether it reflects an abnormal position, size, and/or shape of the epiglottis. It is probably more common in children, which is to be expected considering the normal descent of the larynx with postnatal growth. To date, the condition has not been associated with any significant clinical sequelae.
3D ultrasound simulation based on a biomechanical model of prone MRI in breast cancer imaging

Women with breast cancer, whether screen detected or symptomatic, have both mammography and ultrasound for initial imaging assessment. Unlike X-ray or magnetic resonance, which produce an image of the whole breast, ultrasound provides comparatively limited 2D or 3D views located around the lesions. Combining different modalities is an essential task for accurate diagnosis and simulating ultrasound images based on whole breast data could be a way toward correlating different information about the same lesion. Very few studies have dealt with such a simulation framework since the breast undergoes large scale deformation between the prone position of magnetic resonance imaging and the largely supine or lateral position of ultrasound. We present a framework for the realistic simulation of 3D ultrasound images based on prone magnetic resonance images from which a supine position is generated using a biomechanical model. The simulation parameters are derived from a real clinical infrastructure and from transducers that are used for routine scans, leading to highly realistic ultrasound images of any region of the breast.
Design Principles of Transcriptional Logic Circuits

Using a set of genetic logic gates (AND, OR and XOR), we constructed a binary full-adder. The optimality analysis o f the full-adder showed that, based on the position of the regulation threshold, the system displays different optimal c onfigurations for speed and accuracy under fixed metabolic cost . In addition, the analysis identified an optimal trade-off cu rve bounded by these two optimal configurations. Any configuration outside this optimal trade-off curve is sub-optimal in both speed and accuracy. This type of analysis represents a useful tool for synthetic biologists to engineer faster, mo re accurate and cheaper genes.
Training a text classifier with a single word using Twitter Lists and domain adaptation

Annotating data is a common bottleneck in building text classifiers. This is particularly problematic in social media domains, where data drift requires frequent retraining to maintain high accuracy. In this paper, we propose and evaluate a text classification method for Twitter data whose only required human input is a single keyword per class. The algorithm proceeds by identifying exemplar Twitter accounts that are representative of each class by analyzing Twitter Lists (human-curated collections of related Twitter accounts). A classifier is then fit to the exemplar accounts and used to predict labels of new tweets and users. We develop domain adaptation methods to address the noise and selection bias inherent to this approach, which we find to be critical to classification accuracy. Across a diverse set of tasks (topic, gender, and political affiliation classification), we find that the resulting classifier is competitive with a fully supervised baseline, achieving superior accuracy on four of six datasets despite using no manually labeled data.
A Joint Human/Machine Process for Coding Events and Conflict Drivers

Constructing datasets to analyse the progression of conflicts has been a longstanding objective of peace and conflict studies research. In essence, the problem is to reliably extract relevant text snippets and code (annotate) them using an ontology that is meaningful to social scientists. Such an ontology usually characterizes either types of violent events (killing, bombing, etc.), and/or the underlying drivers of conflict, themselves hierarchically structured, for example security, governance and economics, subdivided into conflict-specific indicators. Numerous coding approaches have been proposed in the social science literature, ranging from fully automated “machine” coding to human coding. Machine coding is highly error prone, especially for labelling complex drivers, and suffers from extraction of duplicated events, but human coding is expensive, and suffers from inconsistency between annotators; thus hybrid approaches are required. In this paper, we analyse experimentally how human input can most effectively be used in a hybrid system to complement machine coding. Using two newly created real-world datasets, we show that machine learning methods improve on rule-based automated coding for filtering large volumes of input, while human verification of relevant/irrelevant text leads to improved performance of machine learning for predicting multiple labels in the ontology.
A Wideband Inductorless LNA With Local Feedback and Noise Cancelling for Low-Power Low-Voltage Applications

A wideband noise-cancelling low-noise amplifier (LNA) without the use of inductors is designed for low-voltage and low-power applications. Based on the common-gate-common-source (CG-CS) topology, a new approach employing local negative feedback is introduced between the parallel CG and CS stages. The moderate gain at the source of the cascode transistor in the CS stage is utilized to boost the transconductance of the CG transistor. This leads to an LNA with higher gain and lower noise figure (NF) compared with the conventional CG-CS LNA, particularly under low power and voltage constraints. By adjusting the local open-loop gain, the NF can be optimized by distributing the power consumption among transistors and resistors based on their contribution to the NF. The optimal value of the local open-loop gain can be obtained by taking into account the effect of phase shift at high frequency. The linearity is improved by employing two types of distortion-cancelling techniques. Fabricated in a 0.13-μm RF CMOS process, the LNA achieves a voltage gain of 19 dB and an NF of 2.8-3.4 dB over a 3-dB bandwidth of 0.2-3.8 GHz. It consumes 5.7 mA from a 1-V supply and occupies an active area of only 0.025 mm2.