nils-herrmann/clean_scirepeval huggingface dataset:
Safe and Reliable Path Planning for the Autonomous Vehicle Verdino

This paper introduces a local planner which computes a set of commands, allowing an autonomous vehicle to follow a given trajectory. To do so, the platform relies on a localization system, a map and a cost map which represents the obstacles in the environment. The presented method computes a set of tentative trajectories, using a schema based on a Frenet frame obtained from the global planner. These trajectories are then scored using a linear combination of weighted cost functions. In the presented approach, new weights are introduced in order to satisfy the specificities of our autonomous platform, Verdino. A study on the influence of the defined weights in the final behavior of the vehicle is introduced. From these tests, several configurations have been chosen and ranked according to two different proposed behaviors. The method has been tested both in simulation and in real conditions.
A longitudinal study of prisoners on remand: psychiatric prevalence, incidence and psychopathology in solitary vs. non-solitary confinement.

To compare two levels of stress (solitary confinement (SC) and non-SC) among remand prisoners as to incidence of psychiatric disorders in relation to prevalent disorders.



Longitudinal repeated assessments were carried out from the start and during the remand phase of imprisonment. Both interview-based and self-reported measures were applied to 133 remand prisoners in SC and 95 remand prisoners in non-SC randomly selected in a parallel study design.



Incidence of psychiatric disorders developed in the prison was significantly higher in SC prisoners (28%) than in non-SC prisoners (15%). Most disorders were adjustment disorders, with depressive disorders coming next. Incident psychotic disorders were rare. The difference regarding incidence was primarily explained by level of stress (i.e. prison form) rather than confounding factors. Quantitative measures of psychopathology (Hamilton Scales and General Health Questionnaire) were significantly higher in subjects with prevalent and incident disorders compared to non-disordered subjects.



Different levels of stress give rise to different incidence of psychiatric morbidity among remand prisoners. The surplus of incident disorders among SC prisoners is related to SC, which may act as a mental health hazard.
Creating Annotated Dialogue Resources: Cross-domain Dialogue Act Classification

This paper describes a method to automatically create dialogue resources annotated with dialogue act information by reusing existing dialogue corpora. Numerous dialogue corpora are available for research purposes and many of them are annotated with dialogue act information that captures the intentions encoded in user utterances. Annotated dialogue resources, however, differ in various respects: data collection settings and modalities used, dialogue task domains and scenarios (if any) underlying the collection, number and roles of dialogue participants involved and dialogue act annotation schemes applied. The presented study encompasses three phases of data-driven investigation. We, first, assess the importance of various types of features and their combinations for effective cross-domain dialogue act classification. Second, we establish the best predictive model comparing various cross-corpora training settings. Finally, we specify models adaptation procedures and explore late fusion approaches to optimize the overall classification decision taking process. The proposed methodology accounts for empirically motivated and technically sound classification procedures that may reduce annotation and training costs significantly.
Adverse drug reactions and drug–drug interactions with over-the-counter NSAIDs

Nonsteroidal anti-inflammatory drugs (NSAIDs) such as ibuprofen have a long history of safe and effective use as both prescription and over-the-counter (OTC) analgesics/antipyretics. The mechanism of action of all NSAIDs is through reversible inhibition of cyclooxygenase enzymes. Adverse drug reactions (ADRs) including gastrointestinal bleeding as well as cardiovascular and renal effects have been reported with NSAID use. In many cases, ADRs may occur because of drug-drug interactions (DDIs) between the NSAID and a concomitant medication. For example, DDIs have been reported when NSAIDs are coadministered with aspirin, alcohol, some antihypertensives, antidepressants, and other commonly used medications. Because of the pharmacologic nature of these interactions, there is a continuum of risk in that the potential for an ADR is dependent on total drug exposure. Therefore, consideration of dose and duration of NSAID use, as well as the type or class of comedication administered, is important when assessing potential risk for ADRs. Safety findings from clinical studies evaluating prescription-strength NSAIDs may not be directly applicable to OTC dosing. Health care providers can be instrumental in educating patients that using OTC NSAIDs at the lowest effective dose for the shortest required duration is vital to balancing efficacy and safety. This review discusses some of the most clinically relevant DDIs reported with NSAIDs based on major sites of ADRs and classes of medication, with a focus on OTC ibuprofen, for which the most data are available.
EmoTweet-28: A Fine-Grained Emotion Corpus for Sentiment Analysis

This paper describes EmoTweet-28, a carefully curated corpus of 15,553 tweets annotated with 28 emotion categories for the purpose of training and evaluating machine learning models for emotion classification. EmoTweet-28 is, to date, the largest tweet corpus annotated with fine-grained emotion categories. The corpus contains annotations for four facets of emotion: valence, arousal, emotion category and emotion cues. We first used small-scale content analysis to inductively identify a set of emotion categories that characterize the emotions expressed in microblog text. We then expanded the size of the corpus using crowdsourcing. The corpus encompasses a variety of examples including explicit and implicit expressions of emotions as well as tweets containing multiple emotions. EmoTweet-28 represents an important resource to advance the development and evaluation of more emotion-sensitive systems.
Neural response correlates of detection of monaurally and binaurally created pitches in humans.

Recent magnetoencephalography (MEG) and functional magnetic resonance imaging studies of human auditory cortex are pointing to brain areas on lateral Heschl's gyrus as the 'pitch-processing center'. Here we describe results of a combined MEG-psychophysical study designed to investigate the timing of the formation of the percept of pitch and the generality of the hypothesized 'pitch-center'. We compared the cortical and behavioral responses to Huggins pitch (HP), a stimulus requiring binaural processing to elicit a pitch percept, with responses to tones embedded in noise (TN)-perceptually similar but physically very different signals. The stimuli were crafted to separate the electrophysiological responses to onset of the pitch percept from the onset of the initial stimulus. Our results demonstrate that responses to monaural pitch stimuli are affected by cross-correlational processes in the binaural pathway. Additionally, we show that MEG illuminates processes not simply observable in behavior. Crucially, the MEG data show that, although physically disparate, both HP and TN are mapped onto similar representations by 150 ms post-onset, and provide critical new evidence that the 'pitch onset response' reflects central pitch mechanisms, in agreement with models postulating a single, central pitch extractor.
Teacher professional learning and development

The International Academy of Education (IAE) is a not-for-profit scientific association that promotes educational research, and its dissemination and implementation. Founded in 1986, the Academy is dedicated to strengthening the contributions of research, solving critical educational problems throughout the world, and providing better communication among policy makers, researchers, and practitioners. The general aim of the IAE is to foster scholarly excellence in all fields of education. Towards this end, the Academy provides timely syntheses of research-based evidence of international importance. The Academy also provides critiques of research and of its evidentiary basis and its application to policy. This booklet about teacher professional learning and development has been prepared for inclusion in the Educational Practices Series developed by the International Academy of Education and distributed by the International Bureau of Education and the Academy. As part of its mission, the Academy provides timely syntheses of research on educational topics of international importance. This is the eighteenth in a series of booklets on educational practices that generally improve learning. This particular booklet is based on a synthesis of research evidence produced for the New Zealand Ministry of Education's Iterative Best Evidence Synthesis (BES) Programme, which is designed to be a catalyst for systemic improvement and sustainable development in education. This synthesis, and others in the series, are available electronically at www.educationcounts.govt.nz/themes/BES. All BESs are written using a collaborative approach that involves the writers, teacher unions, principal groups, teacher educators, academics, researchers, policy advisers, and other interested parties. To ensure its rigour and usefulness, each BES follows national guidelines developed by the Ministry of Education. Professor Helen Timperley was lead writer for the Teacher Professional Learning and Development: Best Evidence Synthesis Iteration [BES], assisted by teacher educators Aaron Wilson and Heather Barrar and research assistant Irene Fung, all of the University of Auckland. The BES is an analysis of 97 studies of professional development that led to improved outcomes for the students of the participating teachers. Most of these studies came from the United States, New Zealand, the Netherlands, the United Kingdom, Canada, and Israel. Dr Lorna Earl provided formative quality assurance for the synthesis; Professor John Hattie and Dr Gavin Brown oversaw the analysis of effect sizes. Helen Timperley is Professor of Education at the University of Auckland. The primary focus of her research is promotion of professional and organizational learning in schools for the purpose of improving student learning. She has …
Virtual reality based neuro-rehabilitation for mental stress reduction

Mental stress is a serious problem experienced by college students which often leads to a decline in academic performance, limited social life, alcoholism and drug abuse. Conventional treatments for coping with mental stress hinge on the affected individual's interaction with a psychiatrist/social support group or indulging in relaxing activities. Virtual Reality therapy (VRT) is an upcoming technique for mental stress treatment, which confines the interaction of the user with a Virtual Environment (VE). In this study, we segregate the participants into two test groups-Control and Stressed. Each participant is induced with stress by means of the Color STROOP task. Then the participant is subjected to a VR-based stress therapy that includes an Island environment, a forest environment and calming instrumental music. Effectiveness of the therapy is assessed using the participant's task performances before and after VRT along with the Positive And Negative Affect Schedule (PANAS)  questionnaire.
CAML: Fast Context Adaptation via Meta-Learning

We propose CAML, a meta-learning method for fast adaptation that partitions the model parameters into two parts: context parameters that serve as additional input to the model and are adapted on individual tasks, and shared parameters that are meta-trained and shared across tasks. At test time, the context parameters are updated with one or several gradient steps on a task-specific loss that is backpropagated through the shared part of the network. Compared to approaches that adjust all parameters on a new task (e.g., MAML), our method can be scaled up to larger networks without overfitting on a single task, is easier to implement, and saves memory writes during training and network communication at test time for distributed machine learning systems. We show empirically that this approach outperforms MAML, is less sensitive to the task-specific learning rate, can capture meaningful task embeddings with the context parameters, and outperforms alternative partitionings of the parameter vectors.
Integrated Terahertz Communication With Reflectors for 5G Small-Cell Networks

As the cellular networks continue to progress between generations, the expectations of 5G systems are planned toward high-capacity communication links that can provide users access to numerous types of applications (e.g., augmented reality and holographic multimedia streaming). The demand for higher bandwidth has led the research community to investigate unexplored frequency spectrums, such as the terahertz band for 5G. However, this particular spectrum is strived with numerous challenges, which includes the need for line-of-sight (LoS) links as reflections will deflect the waves as well as molecular absorption that can affect the signal strength. This is further amplified when a high quality of service has to be maintained over infrastructure that supports mobility, as users (or groups of users) migrate between locations, requiring frequent handover for roaming. In this paper, the concept of mirror-assisted wireless coverage is introduced, where smart antennas are utilized with dielectric mirrors that act as reflectors for the terahertz waves. The objective is to utilize information such as the user's location and to direct the reflective beam toward the highest concentration of users. A multiray model is presented in order to develop the propagation models for both indoor and outdoor scenarios in order to validate the proposed use of the reflectors. An office and a pedestrian-walking scenarios are used for indoor and outdoor scenarios, respectively. The results from the simulation work show an improvement with the usage of mirror-assisted wireless coverage, improving the overall capacity, the received power, the path loss, and the probability of LoS.
Online RGB-D gesture recognition with extreme learning machines

Gesture recognition is needed in many applications such as human-computer interaction and sign language recognition. The challenges of building an actual recognition system do not lie only in reaching an acceptable recognition accuracy but also with requirements for fast online processing. In this paper, we propose a method for online gesture recognition using RGB-D data from a Kinect sensor. Frame-level features are extracted from RGB frames and the skeletal model obtained from the depth data, and then classified by multiple extreme learning machines. The outputs from the classifiers are aggregated to provide the final classification results for the gestures. We test our method on the ChaLearn multi-modal gesture challenge data. The results of the experiments demonstrate that the method can perform effective multi-class gesture recognition in real-time.
The Influence of Transmitter Phase Noise on FMCW Radar Performance

It is generally agreed that the main challenges in designing a frequency modulated continuous wave (FMCW) radar are; (i) frequency sweep linearisation and (ii) controlling leakage of transmitter phase noise into the receiver. This paper addresses the latter and focuses on the seldom mentioned and often neglected problem of reflected noise from large, distant targets causing an increase in the receiver noise floor. Work has been presented since the early 1960s on techniques for reducing the effect of transmitter phase noise leakage desensitising the radar receiver of CW and FMCW radars (O'hara, 1963),(Beasly, 1990). All of this work has concentrated on the problems of direct, short path length leakage around the transmit/receive antenna
On Local Interpolation of DFT Outputs

In many cases frequency estimation of isolated tones can be aided by curve fitting to, or interpolation of interbin energy levels in DFT outputs. Methods of performing DFT magnitude and frequency interpolation are investigated and compared relative to practical realtime applications. Two new methods are proposed which may offer advantages over current approaches: a frequency interpolator which reduces the required number of divide operations with a small increase in MSE, and a new magnitude interpolator which requires no divides. Eric Jacobsen EF Data Corp. 2105 W. 5th Pl. Tempe, AZ 85281 Vox: (602)968-0447 Fax: (602)921-9012 e-mail: ericj@efdata.com, ericj@primenet.com Permission to publish this abstract separately is granted.
Analyzing the Non-Functional Requirements in the Desharnais Dataset for Software Effort Estimation

Studying the quality requirements (aka Non-Functional Requirements (NFR)) of a system is crucial in Requirements Engineering. Many software projects fail because of neglecting or failing to incorporate the NFR during the software life development cycle. This paper focuses on analyzing the importance of the quality requirements attributes in software effort estimation models based on the Desharnais dataset. The Desharnais dataset is a collection of eighty one software projects of twelve attributes developed by a Canadian software house. The analysis includes studying the influence of each of the quality requirements attributes, as well as the influence of all quality requirements attributes combined when calculating software effort using regression and Artificial Neural Network (ANN) models. The evaluation criteria used in this investigation include the Mean of the Magnitude of Relative Error (MMRE), the Prediction Level (PRED), Root Mean Squared Error (RMSE), Mean Error and the Coefficient of determination (R). Results show that the quality attribute “Language” is the most statistically significant when calculating software effort. Moreover, if all quality requirements attributes are eliminated in the training stage and software effort is predicted based on software size only, the value of the error (MMRE) is doubled. KeywordsNon-Functional Requirements, Quality Attributes, Software Effort Estimation, Desharnais Dataset
Optimal Features Set for Extractive Automatic Text Summarization

The goal of text summarization is to reduce the size of the text while preserving its important information and overall meaning. With the availability of internet, data is growing leaps and bounds and it is practically impossible summarizing all this data manually. Automatic summarization can be classified as extractive and abstractive summarization. For abstractive summarization we need to understand the meaning of the text and then create a shorter version which best expresses the meaning, While in extractive summarization we select sentences from given data itself which contains maximum information and fuse those sentences to create an extractive summary. In this paper we tested all possible combinations of seven features and then reported the best one for particular document. We analyzed the results for all 10 documents taken from DUC 2002 dataset using ROUGE evaluation matrices.
A Current Sensor Based on the Giant Magnetoresistance Effect: Design and Potential Smart Grid Applications

Advanced sensing and measurement techniques are key technologies to realize a smart grid. The giant magnetoresistance (GMR) effect has revolutionized the fields of data storage and magnetic measurement. In this work, a design of a GMR current sensor based on a commercial analog GMR chip for applications in a smart grid is presented and discussed. Static, dynamic and thermal properties of the sensor were characterized. The characterizations showed that in the operation range from 0 to ±5 A, the sensor had a sensitivity of 28 mV·A(-1), linearity of 99.97%, maximum deviation of 2.717%, frequency response of −1.5 dB at 10 kHz current measurement, and maximum change of the amplitude response of 0.0335%·°C(-1) with thermal compensation. In the distributed real-time measurement and monitoring of a smart grid system, the GMR current sensor shows excellent performance and is cost effective, making it suitable for applications such as steady-state and transient-state monitoring. With the advantages of having a high sensitivity, high linearity, small volume, low cost, and simple structure, the GMR current sensor is promising for the measurement and monitoring of smart grids.
Evaluation of intrabony defects treated with platelet-rich fibrin or autogenous bone graft: A comparative analysis

The primary objective of this study was to compare clinically and radiographically the efficacy of autologous platelet rich fibrin (PRF) and autogenous bone graft (ABG) obtained using bone scrapper in the treatment of intrabony periodontal defects.



Thirty-eight intrabony defects (IBDs) were treated with either open flap debridement (OFD) with PRF or OFD with ABG. Clinical parameters were recorded at baseline and 6 months postoperatively. The defect-fill and defect resolution at baseline and 6 months were calculated radiographically (intraoral periapical radiographs [IOPA] and orthopantomogram [OPG]).



Significant probing pocket depth (PPD) reduction, clinical attachment level (CAL) gain, defect fill and defect resolution at both PRF and ABG treated sites with OFD was observed. However, inter-group comparison was non-significant (P > 0.05). The bivariate correlation results revealed that any of the two radiographic techniques (IOPA and OPG) can be used for analysis of the regenerative therapy in IBDs.



The use of either PRF or ABG were effective in the treatment of three wall IBDs with an uneventful healing of the sites.
Intrusion detection system by fuzzy interpolation

Network intrusion detection systems identify malicious connections and thus help protect networks from attacks. Various data-driven approaches have been used in the development of network intrusion detection systems, which usually lead to either very complex systems or poor generalization ability due to the complexity of this challenge. This paper proposes a data-driven network intrusion detection system using fuzzy interpolation in an effort to address the aforementioned limitations. In particular, the developed system equipped with a sparse rule base not only guarantees the online performance of intrusion detection, but also allows the generation of security alerts from situations which are not directly covered by the existing knowledge base. The proposed system has been applied to a well-known data set for system validation and evaluation with competitive results generated.
Zero-Knowledge Simulation of Boolean Circuits

A zero-knowledge interactive proof is a protocol by which Alice can convince a polynomially-bounded Bob of the truth of some theorem without giving him any hint as to how the proof might proceed. Under cryptographic assumptions, we give a general technique for achieving this goal for every problem in NP. This extends to a presumably larger class, which combines the powers of non-determinism and randomness. Our protocol is powerful enough to allow Mice to convince Bob of theorems for which she does not even have a proof: it is enough for Alice to convince herself probabilistidly of a theorem, perhaps thanks to her knowledge of some trap-door information, in order for her to be able to convince Bob as well, without compromising the map-door in any way.