nils-herrmann/clean_scirepeval huggingface dataset:
Employing Semantic Context for Sparse Information Extraction Assessment

A huge amount of texts available on the World Wide Web presents an unprecedented opportunity for information extraction (IE). One important assumption in IE is that frequent extractions are more likely to be correct. Sparse IE is hence a challenging task because no matter how big a corpus is, there are extractions supported by only a small amount of evidence in the corpus. However, there is limited research on sparse IE, especially in the assessment of the validity of sparse IEs. Motivated by this, we introduce a lightweight, explicit semantic approach for assessing sparse IE.1 We first use a large semantic network consisting of millions of concepts, entities, and attributes to explicitly model the context of any semantic relationship. Second, we learn from three semantic contexts using different base classifiers to select an optimal classification model for assessing sparse extractions. Finally, experiments show that as compared with several state-of-the-art approaches, our approach can significantly improve the F-score in the assessment of sparse extractions while maintaining the efficiency.
Hybrid Multi-camera Visual Servoing to Moving Target

Visual servoing is a well-known task in robotics. However, there are still challenges when multiple visual sources are combined to accurately guide the robot or occlusions appear. In this paper we present a novel visual servoing approach using hybrid multi-camera input data to lead a robot arm accurately to dynamically moving target points in the presence of partial occlusions. The approach uses four RGBD sensors as Eye-to-Hand (EtoH) visual input, and an arm-mounted stereo camera as Eye-in-Hand (EinH). A Master supervisor task selects between using the EtoH or the EinH, depending on the distance between the robot and target. The Master also selects the subset of EtoH cameras that best perceive the target. When the EinH sensor is used, if the target becomes occluded or goes out of the sensor's view-frustum, the Master switches back to the EtoH sensors to re-track the object. Using this adaptive visual input data, the robot is then controlled using an iterative planner that uses position, orientation and joint configuration to estimate the trajectory. Since the target is dynamic, this trajectory is updated every time-step. Experiments show good performance in four different situations: tracking a ball, targeting a bulls-eye, guiding a straw to a mouth and delivering an item to a moving hand. The experiments cover both simple situations such as a ball that is mostly visible from all cameras, and more complex situations such as the mouth which is partially occluded from some of the sensors.
A Single-Stage Power Electronic Transformer for a Three-Phase PWM AC/AC Drive With Source-Based Commutation of Leakage Energy and Common-Mode Voltage Suppression

This paper presents a novel topology for the generation of adjustable frequency and magnitude pulsewidth-modulated (PWM) three-phase ac from a balanced three-phase ac source with a high-frequency ac link. The proposed single-stage power electronic transformer (PET) with bidirectional power flow capability may find application in compact isolated PWM ac drives. This topology along with the proposed control has the following advantages: 1) input power factor correction; 2) common-mode voltage suppression at the load end; 3) high-quality output voltage waveform (comparable with conventional space vector PWM); and 4) minimization of output voltage loss, common-mode voltage switching, and distortion of the load current waveform due to leakage inductance commutation. A source-based commutation of currents associated with energy in leakage inductance (termed as leakage energy) has been proposed. This results in soft-switching of the output-side converter and recovery of the leakage energy. The entire topology along with the proposed control scheme has been analyzed. The simulation and experimental results verify the analysis and advantages of the proposed PET.
A Recurrent Convolutional Neural Network for Land Cover Change Detection in Multispectral Images

In this paper, we propose a novel network architecture, a recurrent convolutional neural network, which is trained to learn a joint spectral-spatial-temporal feature representation in a unified framework for change detection of multispectral images. To this end, we bring together a convolutional neural network (CNN) and a recurrent neural network (RNN) into one end-to-end network. The former is able to generate rich spectral-spatial feature representations while the latter effectively analyzes temporal dependency in bi-temporal images. Although both CNN and RNN are well-established techniques for remote sensing applications, to the best of our knowledge, we are the first to combine them for multitemporal data analysis in the remote sensing community. Both visual and quantitative analysis of experimental results demonstrates competitive performance in the proposed mode.
A MEMS-Based Flow Rate and Flow Direction Sensing Platform with Integrated Temperature Compensation Scheme

This study develops a MEMS-based low-cost sensing platform for sensing gas flow rate and flow direction comprising four silicon nitride cantilever beams arranged in a cross-form configuration, a circular hot-wire flow meter suspended on a silicon nitride membrane, and an integrated resistive temperature detector (RTD). In the proposed device, the flow rate is inversely derived from the change in the resistance signal of the flow meter when exposed to the sensed air stream. To compensate for the effects of the ambient temperature on the accuracy of the flow rate measurements, the output signal from the flow meter is compensated using the resistance signal generated by the RTD. As air travels over the surface of the cross-form cantilever structure, the upstream cantilevers are deflected in the downward direction, while the downstream cantilevers are deflected in the upward direction. The deflection of the cantilever beams causes a corresponding change in the resistive signals of the piezoresistors patterned on their upper surfaces. The amount by which each beam deflects depends on both the flow rate and the orientation of the beam relative to the direction of the gas flow. Thus, following an appropriate compensation by the temperature-corrected flow rate, the gas flow direction can be determined through a suitable manipulation of the output signals of the four piezoresistors. The experimental results have confirmed that the resulting variation in the output signals of the integrated sensors can be used to determine not only the ambient temperature and the velocity of the air flow, but also its direction relative to the sensor with an accuracy of ± 7.5° error.
Broadcast Cricket Highlight Treatment using a Quadrant Based Method for Global Motion

The Quadrant Based Method (QUADBAM) for Global Motion Estimation introduced here exploits local motion fields. QUADBAM produces similar results to other global motion estimation methods with a lower algorithmic complexity. Two applications of QUADBAM in the treatment of broadcast cricket highlights are presented. An automated highlight detector for broadcast cricket, based solely on QUADBAM generated global motion features, provides acceptable recall rates. Mosaic visualization of individual cricket highlights, created using QUADBAM generated global motion features, are suitable for highlight summaries.
Cloud-Based PaaS Architecture for Service-Oriented Mobile Robots

Open-source software frameworks such as Apache Hadoop and Robot Operating System (ROS) are helped researchers to reduce arduous engineering work releasing them to concentrate more on the core research. The impact of extensive knowledge repositories of robot operating system interconnected with a large distributed data storage framework entails enormous developmental step in the future robotic systems era. Hadoop and ROS integrate the key frameworks presented in this paper to obtain the open source architecture. The main subject of research, an approach to apply service-oriented robotic (SOR) cloud enabling PaaS (Platform-as-a-Service) mobile multi-robot architecture, is presented. Moreover, the focus is based on cloudcommunicating service robots with lightweight workload requirements for the purpose of offloading heavy computation into a cloud. In an integrated experiment, the Ubuntu Linux environment is experimented after installing Hadoop cloud computing platform and ROS. Keywords-robot operating system (ROS); Apache Hadoop; open source; service-oriented robot; cloud robotics; mobile robotics __________________________________________________*****_________________________________________________
Hypothalamic-pituitary-adrenocortical axis: neuropsychiatric aspects.

Evidence of aberrant hypothalamic-pituitary-adrenocortical (HPA) activity in many psychiatric disorders, although not universal, has sparked long-standing interest in HPA hormones as biomarkers of disease or treatment response. HPA activity may be chronically elevated in melancholic depression, panic disorder, obsessive-compulsive disorder, and schizophrenia. The HPA axis may be more reactive to stress in social anxiety disorder and autism spectrum disorders. In contrast, HPA activity is more likely to be low in PTSD and atypical depression. Antidepressants are widely considered to inhibit HPA activity, although inhibition is not unanimously reported in the literature. There is evidence, also uneven, that the mood stabilizers lithium and carbamazepine have the potential to augment HPA measures, while benzodiazepines, atypical antipsychotics, and to some extent, typical antipsychotics have the potential to inhibit HPA activity. Currently, the most reliable use of HPA measures in most disorders is to predict the likelihood of relapse, although changes in HPA activity have also been proposed to play a role in the clinical benefits of psychiatric treatments. Greater attention to patient heterogeneity and more consistent approaches to assessing treatment effects on HPA function may solidify the value of HPA measures in predicting treatment response or developing novel strategies to manage psychiatric disease.
Oxidative stress in ALS: key role in motor neuron injury and therapeutic target.

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disorder characterized by death of motor neurons leading to muscle wasting, paralysis, and death, usually within 2-3 years of symptom onset. The causes of ALS are not completely understood, and the neurodegenerative processes involved in disease progression are diverse and complex. There is substantial evidence implicating oxidative stress as a central mechanism by which motor neuron death occurs, including elevated markers of oxidative damage in ALS patient spinal cord and cerebrospinal fluid and mutations in the antioxidant enzyme superoxide dismutase 1 (SOD1) causing approximately 20% of familial ALS cases. However, the precise mechanism(s) by which mutant SOD1 leads to motor neuron degeneration has not been defined with certainty, and the ultimate trigger for increased oxidative stress in non-SOD1 cases remains unclear. Although some antioxidants have shown potential beneficial effects in animal models, human clinical trials of antioxidant therapies have so far been disappointing. Here, the evidence implicating oxidative stress in ALS pathogenesis is reviewed, along with how oxidative damage triggers or exacerbates other neurodegenerative processes, and we review the trials of a variety of antioxidants as potential therapies for ALS.
An introduction to NDN and its software architecture

The NDN project investigates Jacobson's proposed evolution from today's host-centric network architecture (IP) to a data-centric network architecture (NDN). This conceptually simple shift has far-reaching implications in how we design, develop, deploy and use networks and applications. The NDN design and development has attracted significant attention from the networking community. To facilitate broader participation in addressing NDN research and development challenges, this tutorial will describe the vision of this new architecture and its basic components and operations.
A compact dual-band monopole antenna for 4G LTE and WIFI utilizations

In this paper presented a CPW-fed monopole antenna,which design for WiFi and 4G (LTE) utilizations. The proposed antenna contains a U- slot in the circular patch with gradually varied ground to improve the impedance match, and it occupies a compact size of 42*28.38*1.5mm3. Through simulations and measurements, the proposed antenna with dual-band operation is suitable to for 4G LTE and WiFi utilizations in the 2.28GHz to 2.82GHz band and 3.87GHz to 6.0GHz band.
Microcontroller-based MHz totem-pole PFC with critical mode control

This paper focuses on how to implement critical conduction mode (CRM) control for GaN-based MHz totem-pole PFC with a commercial low-cost microcontroller (MCU), including basic CRM operation, programmed on-time control for achieving low current THD, off-time extension for achieving ZVS for whole line cycle operation, and two-phase interleaving control for input current ripple cancellation and EMI filter size reduction. A novel but simple improvement of zero-current-detection (ZCD) based CRM control method is proposed to solve signal processing delay issue to realize CRM operation for MHz totem-pole PFC. A simple but very accurate on-time calculation method is also proposed for implementing programmed on-time control at MHz high frequency. The result of aforementioned control function integration and implementation is demonstrated on a 1.2kW GaN-based MHz totem-pole PFC prototype.
Ontological approach toward cybersecurity in cloud computing

Widespread deployment of the Internet enabled building of an emerging IT delivery model, i.e., cloud computing. Albeit cloud computing-based services have rapidly developed, their security aspects are still at the initial stage of development. In order to preserve cybersecurity in cloud computing, cybersecurity information that will be exchanged within it needs to be identified and discussed. For this purpose, we propose an ontological approach to cybersecurity in cloud computing. We build an ontology for cybersecurity operational information based on actual cybersecurity operations mainly focused on non-cloud computing. In order to discuss necessary cybersecurity information in cloud computing, we apply the ontology to cloud computing. Through the discussion, we identify essential changes in cloud computing such as data-asset decoupling and clarify the cybersecurity information required by the changes such as data provenance and resource dependency information.
Inhibition of ANO1 by luteolin and its cytotoxicity in human prostate cancer PC-3 cells

Anoctamin 1 (ANO1), a calcium-activated chloride channel, is highly amplified in prostate cancer, the most common form of cancer and leading causes of cancer death in men, and downregulation of ANO1 expression or its functional activity is known to inhibit cell proliferation, migration and invasion in prostate cancer cells. Here, we performed a cell-based screening for the identification of ANO1 inhibitors as potential anticancer therapeutic agents for prostate cancer. Screening of ~300 selected bioactive natural products revealed that luteolin is a novel potent inhibitor of ANO1. Electrophysiological studies indicated that luteolin potently inhibited ANO1 chloride channel activity in a dose-dependent manner with an IC50 value of 9.8 μM and luteolin did not alter intracellular calcium signaling in PC-3 prostate cancer cells. Luteolin inhibited cell proliferation and migration of PC-3 cells expressing high levels of ANO1 more potently than that of ANO1-deficient PC-3 cells. Notably, luteolin not only inhibited ANO1 channel activity, but also strongly decreased protein expression levels of ANO1. Our results suggest that downregulation of ANO1 by luteolin is a potential mechanism for the anticancer effect of luteolin.
Ixodes ricinus (Ixodidae), an occasional phoront on necrophagous and coprophagous beetles in Europe

For ticks, phoretic behaviour using insects associated with vertebrates might offer an alternative strategy to host-seeking. Here we report for the first time the presence of immature stages of the most widespread tick species in Western Europe, Ixodes ricinus (Acari: Ixodidae), on three beetle species belonging to families Silphidae and Geotrupidae (Coleoptera). Specimens were collected while performing fieldwork surveys on insect diversity during the peak of tick’s questing behaviour, in July and August of 2009 and 2010. The collections took place in two Natural Parks, the Aiako Harria, Guipúzcoa in Northern Spain and Wellington Country Park, Berkshire, in England. The silphid beetle Nicrophorus vespilloides and the geotrupid Trypocopris pyrenaeus were collected from pig-carcasses and both carried nymphs of I. ricinus; the geotrupid Anoplotrupes stercorosus was carrying a tick larva while feeding on red deer dung. These findings revealed an unnoticed but common relation of ticks not only with decomposed animals but also with insect scavengers. We discuss the rationale of this phenomenon.
ModelSec: A Generative Architecture for Model-Driven Security

Increasingly, the success of software systems depends largely on how their security requirements are satisfied. However, developers are challenged in implementing these requirements, mainly because of the gap between the specification and implementation, and the technical complexities of the current software infrastructures. Recently, Model-Driven Security has emerged as a new software development area aimed at overcoming these difficulties. This new paradigm takes advantage of the benefits of the model driven software development techniques for modeling and implementing security concerns. Following this trend, this paper proposes a model driven security approach named ModelSec that offers a generative architecture for managing security requirements, from the requirement elicitation to the implementation stage. This architecture automatically generates security software artifacts (e.g. security rules) by means of a model transformation chain composed of two-steps. Firstly, a security infrastructure dependent model is derived from three models, which express the security restrictions, the design decisions and the information needed on the target platform. Then, security software artifacts are produced from the previously generated model. A Domain-Specific Language for security requirements management has been built, which is based on a metamodel specifically designed for this purpose. An application example that illustrates the approach and the Eclipse tools implemented to support it
Annotate-Sample-Average (ASA): A New Distant Supervision Approach for Twitter Sentiment Analysis

The classification of tweets into polarity classes is a popular task in sentiment analysis. State-of-the-art solutions to this problem are based on supervised machine learning models trained from manually annotated examples. A drawback of these approaches is the high cost involved in data annotation. Two freely available resources that can be exploited to solve the problem are: 1) large amounts of unlabelled tweets obtained from the Twitter API and 2) prior lexical knowledge in the form of opinion lexicons. In this paper, we propose Annotate-Sample-Average (ASA), a distant supervision method that uses these two resources to generate synthetic training data for Twitter polarity classification. Positive and negative training instances are generated by sampling and averaging unlabelled tweets containing words with the corresponding polarity. Polarity of words is determined from a given polarity lexicon. Our experimental results show that the training data generated by ASA (after tuning its parameters) produces a classifier that performs significantly better than a classifier trained from tweets annotated with emoticons and a classifier trained, without any sampling and averaging, from tweets annotated according to the polarity of their words.
Context-Based Diversification for Keyword Queries Over XML Data

While keyword query empowers ordinary users to search vast amount of data, the ambiguity of keyword query makes it difficult to effectively answer keyword queries, especially for short and vague keyword queries. To address this challenging problem, in this paper we propose an approach that automatically diversifies XML keyword search based on its different contexts in the XML data. Given a short and vague keyword query and XML data to be searched, we first derive keyword search candidates of the query by a simple feature selection model. And then, we design an effective XML keyword search diversification model to measure the quality of each candidate. After that, two efficient algorithms are proposed to incrementally compute top-k qualified query candidates as the diversified search intentions. Two selection criteria are targeted: the k selected query candidates are most relevant to the given query while they have to cover maximal number of distinct results. At last, a comprehensive evaluation on real and synthetic data sets demonstrates the effectiveness of our proposed diversification model and the efficiency of our algorithms.
A Gradient Tree Boosting based Approach to Rumor Detecting on Sina Weibo

Rumor detecting on microblogging platforms such as Sina Weibo is a crucial issue. Most existing rumor detecting algorithms require a lot of propagation data for model training, thus they do not have good detecting accuracy at the early stage after a rumor message is posted. In this paper, we propose to use gradient tree boosting (GTB) approach to rumor detecting, based on which a rumor detecting algorithm is developed. At the same time, the GTB-based approach makes it easy to conduct feature selection, and a feature selection algorithm is developed. Experiments on a widely used dataset of Sina Weibo show that the proposed detecting algorithm outperforms the state-of-the-art detecting algorithms; moreover, it has the highest detecting accuracy at the early stage. This work seems to be the first to use GTB-based approach in rumor detecting, and the results suggest that it may be a promising one.
Online Model Distillation for Efficient Video Inference

High-quality computer vision models typically address the problem of understanding the general distribution of real-world images. However, most cameras observe only a very small fraction of this distribution. This offers the possibility of achieving more efficient inference by specializing compact, low-cost models to the specific distribution of frames observed by a single camera. In this paper, we employ the technique of model distillation (supervising a low-cost student model using the output of a high-cost teacher) to specialize accurate, low-cost semantic segmentation models to a target video stream. Rather than learn a specialized student model on offline data from the video stream, we train the student in an online fashion on the live video, intermittently running the teacher to provide a target for learning. Online model distillation yields semantic segmentation models that closely approximate their Mask R-CNN teacher with 7 to 17× lower inference runtime cost (11 to 26× in FLOPs), even when the target video’s distribution is non-stationary. Our method requires no offline pretraining on the target video stream, and achieves higher accuracy and lower cost than solutions based on flow or video object segmentation. We also provide a new video dataset for evaluating the efficiency of inference over long running video streams.