nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning

Vehicles of higher automation levels require the creation of situation awareness. One important aspect of this situation awareness is an understanding of the current risk of a driving situation. In this work, we present a novel approach for the dynamic risk assessment of driving situations based on images of a front stereo camera using deep learning. To this end, we trained a deep neural network with recorded monocular images, disparity maps and a risk metric for diverse traffic scenes. Our approach can be used to create the aforementioned situation awareness of vehicles of higher automation levels and can serve as a heterogeneous channel to systems based on radar or lidar sensors that are used traditionally for the calculation of risk metrics.
EMARS: Efficient Management and Allocation of Resources in Serverless

We introduce EMARS, an efficient resource management system for serverless cloud computing frameworks with the goal to enhance resource (focus on memory) allocation among containers. We have built our prototype on top of an open-source serverless platform, OpenLambda. It is based upon application workloads and serverless functions' memory needs. As a background motivation we analyzed the latencies and memory requirements of functions running on AWS lambda. The memory limits also lead to variations in number of containers spawned on OpenLambda. We use memory limit settings to propose a model of predictive efficient memory management.
A Study of Consumers' Trust in Online Shopping between Pick-up Goods Behavior in the Convenience Stores

With the adoption of Internet, online shopping has provided a convenience way to purchase goods or services from anywhere at any time in recent years globally. Especially, convenience stores are available for consumers to pick up goods ordered from Internet shops in Taiwan. Therefore, convenience stores have become an important success factor for increasing a lot of profit in online shopping in Taiwan. Past researches have indicated that consumers dare not or are not willing to purchase goods in online shopping. The reasons include the problem of security and the lack of consumers' trust. Thus, these problems constitute a key barrier to the use of online shopping as well as long-term commitment to the relationship building. Therefore, there is a need to build up consumers' trust in order to overcome the influential factor in online shopping. In this paper, we use statistic analysis method by questionnaires to discuss the characteristics of pick-up goods in the convenience stores and illustrate the relationship among consumers' trust in online shopping and pick-up goods behavior in convenience stores. In our experiment, questionnaire items are measured by Likert scale. In 227 valid questionnaires, 90% participants deeply believe that pick-up goods in the convenience stores can promote consumers' trust in online shopping.
Rectangular patch antenna with inset feed and modified ground-plane for wideband antenna

This paper presents the technique for increasing bandwidth of rectangular patch antenna from 0.88 GHz (7.76 - 8.64 GHz) to 6.75 GHz (3.49 - 10.24 GHz). This technique use inset feed patch antenna with modified ground plane for achieved widest bandwidth. We will propose three types of rectangular patch antenna: the simple rectangular patch fed by microstrip line, inset feed rectangular patch and inset feed rectangular patch with modifies ground plane. The final simulation result show that the lower edge of frequency is moved down from 7.76 GHz to 3.49 GHz and the higher edge of frequency is shifted up from 8.64 GHz to 10.24 GHz, which is the one selection for increasing bandwidth of rectangular patch antenna for wideband. Details of the increasing bandwidth of microstrip patch antenna are described, and simulation results for obtained wideband performance are presented by using IE3D Zeland software.
The first step in the biosynthesis of cocaine in Erythroxylum coca: the characterization of arginine and ornithine decarboxylases

Despite the long history of cocaine use among humans and its social and economic significance today, little information is available about the biochemical and molecular aspects of cocaine biosynthesis in coca (Erythroxylum coca) in comparison to what is known about the formation of other pharmacologically-important tropane alkaloids in species of the Solanaceae. In this work, we investigated the site of cocaine biosynthesis in E. coca and the nature of the first step. The two principal tropane alkaloids of E. coca, cocaine and cinnamoyl cocaine, were present in highest concentrations in buds and rolled leaves. These are also the organs in which the rate of alkaloid biosynthesis was the highest based on the incorporation of 13CO2. In contrast, tropane alkaloids in the Solanaceae are biosynthesized in the roots and translocated to the leaves. A collection of EST sequences from a cDNA library made from young E. coca leaves was employed to search for genes encoding the first step in tropane alkaloid biosynthesis. Full-length cDNA clones were identified encoding two candidate enzymes, ornithine decarboxylase (ODC) and arginine decarboxylase (ADC), and the enzymatic activities of the corresponding proteins confirmed by heterologous expression in E. coli and complementation of a yeast mutant. The transcript levels of both ODC and ADC genes were highest in buds and rolled leaves and lower in other organs. The levels of both ornithine and arginine themselves showed a similar pattern, so it was not possible to assign a preferential role in cocaine biosynthesis to one of these proteins.
Input-series output-parallel AC/AC converter

Input-series and output-parallel (ISOP) converters are suitable for the high input voltage and low output voltage conversion applications. An ISOP current mode AC/AC converter with high frequency link is proposed. The control strategy and operation principle of the ISOP AC/AC converter are investigated. The control strategy ensures the proper sharing of the input voltage and the proper sharing of the output current among the ISOP AC/AC converter. By simulation, the correctness of the operation principle and the control strategy of the ISOP AC/AC converter are proved.
Mining and ranking users’ intents behind queries

How to understand intents behind user queries is crucial towards improving the performance of Web search systems. NTCIR-11 IMine task focuses on this problem. In this paper, we address the NTCIR-11 IMine task with two phases referred to as Query Intent Mining (QIM) and Query Intent Ranking (QIR). (I) QIM is intended to mine users’ potential intents by clustering short text fragments related to the given query. (II) QIR focuses on ranking those mined intents in a proper way. Two challenges exist in handling these tasks.
DC Voltage Balancing for PWM Cascaded H-Bridge Converter Based STATCOM

This paper presents a new control method for cascaded connected H-bridge converter based STATCOMs. These converters have been classically commutated at fundamental line-frequencies, but the evolution of power semiconductors has allowed to increase switching frequencies and the power ratings of these devices, permitting the use of PWM modulation techniques. This paper mainly focuses the DC bus voltage balancing problems, and proposes a new control technique that solves these balancing problems maintaining the delivered reactive power equally distributed among all the H-bridges of the converter
Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin

We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech—two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of speech including noisy environments, accents and different languages. Key to our approach is our application of HPC techniques, resulting in a 7x speedup over our previous system [26]. Because of this efficiency, experiments that previously took weeks now run in days. This enables us to iterate more quickly to identify superior architectures and algorithms. As a result, in several cases, our system is competitive with the transcription of human workers when benchmarked on standard datasets. Finally, using a technique called Batch Dispatch with GPUs in the data center, we show that our system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.
Fall detection using ceiling-mounted 3D depth camera

This paper proposes an algorithm for fall detection using a ceiling-mounted 3D depth camera. The lying pose is separated from common daily activities by a k-NN classifier, which was trained on features expressing head-floor distance, person area and shape's major length to width. In order to distinguish between intentional lying postures and accidental falls the algorithm also employs motion between static postures. The experimental validation of the algorithm was conducted on realistic depth image sequences of daily activities and simulated falls. It was evaluated on more than 45000 depth images and gave 0% error. To reduce the processing overload an accelerometer was used to indicate the potential impact of the person and to start an analysis of depth images.
Depressive realism: a meta-analytic review.

The current investigation represents the first meta-analysis of the depressive realism literature. A search of this literature revealed 75 relevant studies representing 7305 participants from across the US and Canada, as well as from England, Spain, and Israel. Results generally indicated a small overall depressive realism effect (Cohen's d=-.07). Overall, however, both dysphoric/depressed individuals (d=.14) and nondysphoric/nondepressed individuals evidenced a substantial positive bias (d=.29), with this bias being larger in nondysphoric/nondepressed individuals. Examination of potential moderator variables indicated that studies lacking an objective standard of reality (d=-.15 versus -.03, for studies possessing such a standard) and that utilize self-report measures to measure symptoms of depression (d=.16 versus -.04, for studies which utilize structured interviews) were more likely to find depressive realism effects. Methodological paradigm was also found to influence whether results consistent with depressive realism were found (d's ranged from -.09 to .14).
Investments in information systems and technology in the healthcare: Project management mediation

Healthcare organisations must improve their business practices and internal procedures in order to answer the increasing demand of health professionals and the general public for more and better information. Hospitals invest massively in information systems and technology (IS/IT) in the hope that these investments will improve healthcare and meet patients’ demands. The main objective of our research is to study how organisational maturity, enhanced by investments in IS/IT, project management and best practices, leads to successful projects in public healthcare organisations. The rational of our model is that organisational maturity has a positive effect on IS/IT project success, and that this success is also positively enhanced by the use of project management practices. We emphasise that this combination of approaches can increase the effectiveness of projects. Furthermore, it can also improve the confidence that the results of investments will meet stakeholders’ expectations.
Wearable Glove-Type Driver Stress Detection Using a Motion Sensor

Increased driver stress is generally recognized as one of the major factors leading to road accidents and loss of life. Even though physiological signals are reported as the most reliable means to measure driver stresses, they often require the use of unique and expensive sensors, which produce dynamic and varying readings within individuals. This paper presents a novel means to predict a driver’s stress level by evaluating the movement pattern of the steering wheel. This is accomplished by using an inertial motion unit sensor, which is placed on a glove worn by the driver. The motion sensor selected for this paper was chosen because for its low cost and the fact that it is least affected by environmental factors as compared with a physiological signal. Experiments were conducted in three different environmental scenarios. The scenarios were classified as “urban,” “highway,” and “rural,” and they were chosen to simulate contrasting stress conditions experienced by the driver. In this paper, skin conductance and driver self-reports served as a reference stress to predict the driver’s stress level. Galvanic skin response, a well-known stress indicator, was captured along the driver’s palm and the readings were transmitted to a mobile device via low energy Bluetooth for further processing. The results revealed that indirect measurement of steering wheel movement with an inertial motion sensor could obtain accuracies up to an average rate of 94.78%. This demonstrates the opportunity for inclusion of motion sensors in wireless driver assistance systems for ambulatory monitoring of stress levels.
Deterministic Dependency Parsing of English Text

This paper presents a deterministic dependency parser based on memory-based learning, which parses English text in linear time. When trained and evaluated on the Wall Street Journal section of the Penn Treebank, the parser achieves a maximum attachment score of 87.1%. Unlike most previous systems, the parser produces labeled dependency graphs, using as arc labels a combination of bracket labels and grammatical role labels taken from the Penn Treebank II annotation scheme. The best overall accuracy obtained for identifying both the correct head and the correct arc label is 86.0%, when restricted to grammatical role labels (7 labels), and 84.4% for the maximum set (50 labels).
Federated Meta-Learning for Recommendation

Recommender systems have been widely studied from the machine learning perspective, where it is crucial to share information among users while preserving user privacy. In this work, we present a federated meta-learning framework for recommendation in which user information is shared at the level of algorithm, instead of model or data adopted in previous approaches. In this framework, user-specific recommendation models are locally trained by a shared parameterized algorithm, which preserves user privacy and at the same time utilizes information from other users to help model training. Interestingly, the model thus trained exhibits a high capacity at a small scale, which is energyand communicationefficient. Experimental results show that recommendation models trained by meta-learning algorithms in the proposed framework outperform the state-of-the-art in accuracy and scale. For example, on a production dataset, a shared model under Google Federated Learning (McMahan et al., 2017) with 900,000 parameters has prediction accuracy 76.72%, while a shared algorithm under federated meta-learning with less than 30,000 parameters achieves accuracy of 86.23%.
An algorithmic overview of surface registration techniques for medical imaging

This paper presents a literature survey of automatic 3D surface registration techniques emphasizing the mathematical and algorithmic underpinnings of the subject. The relevance of surface registration to medical imaging is that there is much useful anatomical information in the form of collected surface points which originate from complimentary modalities and which must be reconciled. Surface registration can be roughly partitioned into three issues: choice of transformation, elaboration of surface representation and similarity criterion, and matching and global optimization. The first issue concerns the assumptions made about the nature of relationships between the two modalities, e.g. whether a rigid-body assumption applies, and if not, what type and how general a relation optimally maps one modality onto the other. The second issue determines what type of information we extract from the 3D surfaces, which typically characterizes their local or global shape, and how we organize this information into a representation of the surface which will lead to improved efficiency and robustness in the last stage. The last issue pertains to how we exploit this information to estimate the transformation which best aligns local primitives in a globally consistent manner or which maximizes a measure of the similarity in global shape of two surfaces. Within this framework, this paper discusses in detail each surface registration issue and reviews the state-of-the-art among existing techniques.
Rotor structure in 50 kW spoke-type interior permanent magnet synchronous motor with ferrite permanent magnets for automotive applications

In light of global warming and the rapidly increasing prices of rare earths, electric vehicles with motors free of rare earths are needed. We are therefore developing an interior permanent magnet synchronous motor (IPMSM) that utilizes ferrite permanent magnets. Because the residual flux density of a ferrite magnet is lower than that of a rare earth magnet, we have adopted a spoke-type IPMSM with radially arranged ferrite magnets. This paper proposes two rotor structure designs for spoke-type IPMSMs with ferrite magnets, and presents the results of two-dimensional finite element analysis (FEA) for the two designs. The results indicate that the performance loss from using ferrite magnets can be moderated by effectively utilizing reluctance torque in a spoke-type IPMSM. Finally, a rotor structure that has sufficient performance to be usable in a traction motor free of rare earths is examined through experiments on a prototype and three-dimensional FEA.
How Alluring Are Dark Personalities ? The Dark Triad and Attractiveness in Speed Dating

Dark Triad traits (narcissism, psychopathy, and Machiavellianism) are linked to the pursuit of short-term mating strategies, but they may have differential effects on actual mating success in naturalistic scenarios: Narcissism may be a facilitator for men’s short-term mating success, while Machiavellianism and psychopathy may be detrimental. To date, little is known about the attractiveness of Dark Triad traits in women. In a speed-dating study, we assessed participants’ Dark Triad traits, Big Five personality traits, and physical attractiveness in N=90 heterosexual individuals (46 women and 44 men). Each participant rated each partner’s mate appeal for shortand long-term relationships. Across both sexes, narcissism was positively associated with mate appeal for shortand long-term relationships. Further analyses indicated that these associations were due to the shared variance among narcissism and extraversion in men and narcissism and physical attractiveness in women, respectively. In women, psychopathy was also positively associated with mate appeal for short-term relationships. Regarding mating preferences, narcissism was found to involve greater choosiness in the rating of others’ mate appeal (but not actual choices) in men, while psychopathy was associated with greater openness towards short-term relationships in women.
An Unconstrained Dataset for Non-Stationary Video Based Fire Detection

Challenging ground truth and standardized metrics are a mandatory requirement for the development and evaluation of computer vision algorithms. Despite the significant amount of publications on video based fire detection research it remains difficult to compare different algorithms due to the lack of common evaluation schemes and evaluation datasets. We address both of these issues by presenting a new dataset of fire videos containing frame by frame annotations which may be used for non-stationary fire detection algorithms evaluation. The dataset includes hand-held, robot attached and drone attached footages and aims to boost the development of fully autonomous fire fighter robots. The presented ground truth and metrics may adapt to any state-of-the-art technique and provide a reliable and unbiased solution to compare them.
Arc Flash Calculations for a 1.3-MW Photovoltaic System

When planning for the installation of a 1.3-MW photovoltaic (PV) system and its integration into an existing facility's electrical system, an arc flash study was performed to determine the best protective device settings to minimize the arc flash energy in the PV system and to determine the effect of the additional short-circuit current on arc flash calculations for the existing system. The existing system has distribution at 13.8 kV and two 10-MVA transformers to a 2.4-kV feeder system. The PV system adds the hazard of 500-Vdc subarray collection wiring and terminations. The dc arc flash exposure of PV systems, including those in the inverter enclosures, can be complicated, and this paper outlines the basic data collected for the study, the steps taken to complete the calculations, the methodology used, the results, and the team's learnings.
Review on virtual synchronous generator (VSG) for enhancing performance of microgrid

Increasing penetration level of Distributed Generators (DGs)/ Renewable energy sources (RES) creates low inertia problems, damping effect on grid dynamic performance and stability. Voltage rise by reverse power from PV generations, excessive supply of electricity in the grid due to full generation of the DGs, power fluctuations due to variable nature of Renewable energy sources, and degradation of frequency regulation can be considered as some negative results of mentioned hurdle. For securing such a grid there should add inertia, virtually. The proposed control approach based on an inverter connected in Microgrid, which operated as a virtual synchronous generator (VSG) reacting only in transitory situations. Super capacitor (SC), an energy storage system (ESS) included in MG-forming inverter i.e., a normal inverter structure used for contributing system robustness and reliability. In steady-state load distributed between the MG-supporting inverter are achieved with two restoration controllers for active and reactive powers. The virtual inertia hypothesis can be used for maintaining a large share of DGs in future grids without affect system stability. This proposed system analyzed and simulated using MATLAB/SIMULINK.
A Synchronization Technique for Bidirectional IPT Systems

Bidirectional inductive power transfer (IPT) systems are attractive for applications such as electric vehicles and vehicle-to-grid systems which preferably require “contactless” and two-way power transfer. However, in contrast to unidirectional IPT systems, bidirectional IPT systems require more sophisticated control strategies to control the power flow. An indispensible component of such control strategies is the robust and accurate synchronization between the primary- and pickup-side converters, without which the transfer of real power in any direction cannot be guaranteed. This paper proposes a novel technique that synchronizes converters on both the primary and pickup sides of bidirectional IPT systems. The technique uses an auxiliary winding, located on the pickup side, to produce a synchronizing signal which, in turn, can be utilized to regulate the real power flow. This paper also presents a mathematical model for the proposed technique and investigates its sensitivity for component tolerances. The viability of the technique, which is applicable to both single- and multiple-pickup IPT systems, is demonstrated through both simulations and experimental results of a 1-kW prototype bidirectional IPT system.