nils-herrmann/clean_scirepeval huggingface dataset:
Research and Application of Traffic Sign Detection and Recognition Based on Deep Learning
Venue: 2018 International Conference on Robots Intelligent System (ICRIS)
Year: 2018

Nowadays, with the rapid development of society and economy, automobiles have become almost one of the convenient modes of transport for every household. This makes the road traffic environment more and more complicated, and people expect to have an intelligent Vision assisted applications that provide drivers with traffic sign information, regulate driver operations, or assist in vehicle control to ensure road safety. As one of the more important functions, traffic sign detection and recognition[1] has become a hot research direction of researchers at home and abroad. It is mainly the use of vehicle cameras to capture real time road images, and then to detect and identify the traffic signs encountered on the road, thus providing accurate information to the driving system. However, the road conditions in the actual scene are very complicated. After many years of hard work, researchers have not yet made the recognition system practical, and further research and improvement are still needed. Traditionally, traffic signage has been detected and categorized using standard computer vision methods, but it also takes considerable time to manually process important features of the image. With the development and progress of science and technology, more and more scholars use deep learning technology to solve this problem. The main reason that the deep learning method is widely accepted is that the model can learn the deep features inside the image autonomously from the training samples, especially for many cases that do not know how to design the feature extractor, such as expression recognition, target detection Wait. Based on the application of road traffic sign detection and recognition, this article focuses on the correctness and high efficiency of detection and recognition. Through Caffe[2] which is the open source framework, a deep convolution neural network algorithm is proposed to train traffic sign training sets to get a model that can classify traffic signs and to learn and identify the most critical of these traffic signs Features, so as to achieve the purpose of identifying traffic signs in the real scene.
Do Women in Leadership Reduce Sexual Harassment Claims on College Campuses?
Year: 2020

This article analyzes whether the representation of women in leadership roles reduces sexual harassment claims on college campuses. We test competing claims regarding the impact of women's workplace authority on sexual harassment. Our framework draws on the women as agents of change and power paradox perspectives to interrogate the role of gender and power in reducing workplace harassment in institutions of higher education. We find that women's overall integration into upper administrative positions reduces harassment claims. However, we also find that the gender of the president and the Title IX officer is not significantly related to the number of harassment claims. We consider the implications of these findings for ongoing efforts to reduce harassment on college and university campuses.
Time Specific Encryption with Constant Size Secret Keys Secure under Standard Assumption
Venue: IACR Cryptol. ePrint Arch.
Year: 2020

In Time Specific Encryption (TSE) [Paterson&Quaglia, SCN'10] system, each secret key (resp. ciphertext) is associated with a time period t [0,T 1] (resp. a time interval [L,R] where L,R [0,T 1] A ciphertext under [L,R] is correctly decrypted by any secret key for any time t included in the interval, i.e. t [L,R] TSE's generic construction from identity based encryption (IBE) (resp. hierarchical IBE (HIBE) from which we obtain a concrete TSE scheme with secret keys of size O(log T |g| (resp. O(log T |g| and ciphertexts of size O(log T |g| (resp. O(1)|g| has been proposed in [Paterson&Quaglia, SCN'10] (resp.  where |g| denotes bit length of an element in a bilinear groupG. In this paper, we propose another TSE's generic construction from wildcarded identity based encryption (WIBE) Differently from the original WIBE [Abdalla et al. ICALP'06] we consider WIBE w/o (hierarchical) key delegatability. By instantiating the TSE's generic construction, we obtain the first concrete scheme with constant size secret keys secure under a standard (static) assumption. Specifically, it has secret keys of size O(1)|g| and ciphertexts of size O(log T |g| and achieves security under the decisional bilinear Diffie Hellman (DBDH) assumption.
N2C: Neural Network Controller Design Using Behavioral Cloning
Venue: IEEE Transactions on Intelligent Transportation Systems
Year: 2021

Modern vehicles communicate data to and from sensors, actuators, and electronic control units (ECUs) using Controller Area Network (CAN) bus, which operates on differential signaling. An autonomous ECU responsible for the execution of decision commands to an autonomous vehicle is developed by assimilating the information from the CAN bus. The conventional way of parsing the decision commands is motion planning, which uses a path tracking algorithm to evaluate the decision commands. This study focuses on designing a robust controller using behavioral cloning and motion planning of autonomous vehicle using a deep learning framework. In the first part of this study, we explore the pipeline of parsing decision commands from the path tracking algorithm to the controller and proposed a neural network based controller $N^{2}C$ using behavioral cloning. The proposed network predicts throttle, brake, and torque when trained with the manual driving data acquired from the CAN bus. The efficacy of the proposed method is demonstrated by comparing the accuracy with the Proportional Derivative Integral (PID) controller in conjunction with the path tracking algorithm (pure pursuit and model predictive control based path follower) The second part of this study complements $N^{2}C$ in which an end to end neural network for predicting the speed and steering angle is proposed with image data as an input. The performance of the proposed frameworks are evaluated in real time and on the Udacity dataset, showing better metric scores in the former and reliable prediction in the later case when compared with the state of the art methods.
The International Dimension of Inflation: Evidence from Disaggregated Consumer Price Data
Year: 2009

We estimate the contribution of international common factors to the dynamics of price inflation rates of a cross section of 948 CPI products in four OECD countries: United States, Germany, France, and United Kingdom. We find two main results. First, on average, and at least in the sample 1991 2004, one international common factor explains between 15% and 30% of the variance of consumer prices (depending on the transformation applied to the data) Given the high level of disaggregation of our panel, this estimate is best viewed as a "lower bound" for the contribution of international factors to inflation dynamics. Second, we find a strongly positive and statistically significant relationship between exposure of consumer inflation to international shocks and trade openness at the sectoral level. The latter result holds regardless of whether the original data are expressed in local as opposed to common currency.
Multi Level Anomaly Detection on Streaming Graph Data
Venue: ArXiv
Year: 2014

As a natural structure for representing entities and interactions, graphs are commonly used in many domains. Because of inherent complexity, converting graph data to meaningful information through analysis or visualization is often challenging. Identifying patterns and aberrations in graph data can pinpoint areas of interest, provide context for deeper understanding, and enable discovery in many applications. This work presents a novel modeling and analysis framework for graph sequences. The framework addresses the issues of modeling, detecting anomalies at multiple scales, and enabling understanding of graph data. A new graph model, generalizing the BTER model of Seshadhri et al. by adding flexibility to community structure, is introduced and used to perform multi scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating probabilities at finer levels, and these closely related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into the graph's structure and internal context that may shed light on a detected event. Additionally, this multi scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline on a series of sampled graphs. The superior hierarchical detector outperforms the baseline, and changes in community structure are accurately detected at the node, subgraph, and graph levels. To illustrate the accessibility of information made possible via this technique, a prototype visualization tool, informed by the multi scale analysis is tested on NCAA football data. Teams and conferences exhibiting changes in membership are identified with greater than 92% precision and recall. Screenshots of an interactive visualization, allowing users to probe into selected communities, are given. Lead author.
Effective communication with older people.
Venue: Nursing older people
Year: 2019

Effective communication with older people is an important aspect of nursing practice. Ineffective communication can lead to older people feeling inadequate, disempowered and helpless. Nurses have a duty to ensure that older people think they are being listened to and that their concerns are being validated in a non judgemental way. Central to effective communication is the ability of nurses to be self aware, and monitor their thoughts and feelings about, for example, negative stereotypes associated with the ageing process. Effective communication can sometimes be difficult to achieve due to the effects of ageing, but nurses can overcome some barriers through thoughtful interventions. It is important to treat older people as individuals, and to monitor and adapt communication accordingly. By doing so, nurses can ensure older people feel empowered, respected and able to maintain their independence.
Conceptualizing and Operationalizing a Formative Assessment Model for English Chinese Consecutive Interpreting: A Case Study in an Undergraduate Interpreting Course
Year: 2019

Formative assessment has been increasingly used by interpreter trainers and educators to promote student learning. Different forms of formative assessment have been practiced and reported in interpreting literature. However, a critical review of current practices reported in literature suggests that a longitudinally designed formative assessment model that harnesses the synergistic potential of self, peer, and teacher assessment seems to be lacking. This chapter therefore aims to provide a detailed account of how an inclusive formative assessment model was conceptualized and operationalized for an undergraduatelevel English Chinese consecutive interpreting course and how students and the teacher perceived the assessment model. Based on the students' evaluation and the teacher's reflection, the chapter highlights good practices that contribute to effective formative assessment, discusses potential problems, proposes possible solutions, and suggests future trends in implementing and researching formative assessment in interpreter training and education. Conceptualizing and Operationalizing a Formative Assessment Model for English Chinese Consecutive Interpreting: A Case Study in an Undergraduate Interpreting Course
End to End View Synthesis for Light Field Imaging with Pseudo 4DCNN
Venue: ECCV
Year: 2018

Limited angular resolution has become the main bottleneck of microlens based plenoptic cameras towards practical vision applications. Existing view synthesis methods mainly break the task into two steps, i.e. depth estimating and view warping, which are usually inefficient and produce artifacts over depth ambiguities. In this paper, an end to end deep learning framework is proposed to solve these problems by exploring Pseudo 4DCNN. Specifically, 2D strided convolutions operated on stacked EPIs and detail restoration 3D CNNs connected with angular conversion are assembled to build the Pseudo 4DCNN. The key advantage is to efficiently synthesize dense 4D light fields from a sparse set of input views. The learning framework is well formulated as an entirely trainable problem, and all the weights can be recursively updated with standard backpropagation. The proposed framework is compared with state of the art approaches on both genuine and synthetic light field databases, which achieves significant improvements of both image quality +2 dB higher) and computational efficiency (over 10X faster) Furthermore, the proposed framework shows good performances in real world applications such as biometrics and depth estimation.
Performance analysis for subsea blind shear ram preventers subject to testing strategies
Venue: Reliab. Eng. Syst. Saf.
Year: 2018

In a subsea blowout preventer system, a subsea blind shear ram preventer (BSRP) plays as a crucial safety barrier by cutting off the drill pipe and sealing the wellhead to prevent serious accidents. Testing and repairs of BSRPs are the main issues in operation and maintenance activities. It is important to assess BSRPs unavailability during proof and partial testing phases in order to ensure their safety functions. This paper presents a newly state based approach for unavailability analysis by incorporating testing activities of BSRPs into multiphase Markov process. In the approach, states waiting for repair are taken into consideration. Analytical formulas for evaluation of time dependent unavailability and average unavailability for BSRPs are developed. In addition, the non periodic characteristics and effects of degradation are also taken into account in proof testing. The effects of testing errors and postponed repairs on the tendency of unavailability in partial testing phases are checked in the proposed models. Performance analyses for BSRPs configurations, scenarios and cases considered in the paper are carried out to demonstrate the application of the proposed models. Monte Carlo models for both proof and partial testing are developed and simulated. Different comparisons are performed for validation of the set of the derived formulations.
Glossopharyngeal breathing can allow a lung expansion greater than inspiratory capacity in muscular dystrophy
Venue: European Respiratory Journal
Year: 2019

Glossopharyngeal breathing (GPB) also called "frog breathing" is a positive pressure breathing technique that uses muscles of the mouth and pharynx to propel small volumes of air "gulps" into the lungs. It consists of three phases: 1) mouth opening so that air enters the oral and pharyngeal cavities; 2) mouth closure with tongue and pharyngeal muscles propelling a gulp of air through the open glottis raising intrathoracic pressure; 3) glottis closure to ensure the air trapped into the lungs [1, 2] These three phases are repeated several times until a comfortable lung expansion is reached. GPB is known to be able to increase lung volumes above physiological total lung capacity (TLC) therefore increasing vital capacity [2 5] This is a consequence of an increase in lung volume, but also of intrapulmonary gas compression. In muscular dystrophy, glossopharyngeal breathing is effective in presence of severe dysphagia and diaphragmatic weakness: it recruits the lung above IC, reversing paradoxical respiratory abdominal motion with a presumably more homogeneously distributed pulmonary ventilation http:/bit.ly/2LyXDEL
Task based language teaching: sorting out the misunderstandings
Year: 2009

This paper begins by offering a definition of 'task' and by emphasizing that there is no single 'task based teaching' approach. It then evaluates a number of criticisms of TBT, drawing on recent critiques by Widdowson, Seedhouse, Sheen, and Swan. It is argued that many of these criticisms stem from a fundamental misunderstanding of what a 'task' is, and of the theoretical rationales that inform task based teaching. These criticisms also reflect a failure to acknowledge that multiple versions of task based teaching exist. In particular, it is argued that task based teaching need not be seen as an alternative to more traditional, form focused approaches but can be used alongside them. The paper concludes with an examination of a number of genuine problems with implementing task based teaching, as reflected in evaluation studies.
Microfinance accountability in Cameroon: A cure or a curse for poverty alleviation?
Year: 2017

The purpose of this study is to use empirical findings to identify the different forms of accountability practices existing in Cameroon microfinance institutions (MFIs) and explore how such practices have evolved and institutionalised within the microfinance sector in Cameroon through time.  This study is designed to investigate if the institutionalised accountability practices within the microfinance sector in Cameroon are a cure or a curse for poverty alleviation. This study is based on the new institutional sociology (NIS) and on a case study approach and combines in depth interviews and secondary data sources.  This study identifies three principal forms of accountability practices common with MFIs in Cameroon: dysfunctional, manipulative and dribbling accountabilities.  This paper is novel because it extends the NIS into the microfinance sector and explains how conflicting institutional pressures resulting from differences of accountability practices can be resolved and also exposes the unintended consequences of both resistance and passive actions of local actors on microfinance, the poor and poverty alleviation.
Full Face Rejuvenation with Hyaluronic Acid Fillers based on the MD Codes Technique: A Retrospective, Single Center Study
Year: 2020

Increased standardization may be necessary to maximize benefits and minimize complications with fullface rejuvenation using hyaluronic acid (HA) fillers. The aim of this study was to assess the utility and safety of the standardized MD Codes approach.  This was a retrospective review of data from 84 patients receiving full face rejuvenation with HA fillers using the MD Codes technique within a single treatment plan, across two visits separated by ~1 month.  Participants had a mean age of 49.1 9.9 years, and 83 (98.8% were female. They were treated with 527 syringes of filler (6.3 1.8 per patient) 334 syringes (63.4% 4.0 1.5 per patient) during visit 1 and 193 (36.6% 2.3 1.1 per patient) during visit 2. The most commonly treated areas were the cheek (n=84; 100% and nasolabial fold (n=26; 31.0% at visit 1, and the nasolabial fold (n=36; 43.9% and tear trough (n=35; 42.7% at visit 2. Eight adverse events were recorded in 6 patients (7.1% bruising, n=4; pain, n=4. All were minor and transient. There were no lateonset complications.  Full face rejuvenation using the MD Codes technique can be successfully performed with minimal complications. This approach may define a new standard of care.
STELLAR POPULATIONS IN COMPACT GALAXY GROUPS: A MULTI WAVELENGTH STUDY OF HCGs 16, 22, AND 42, THEIR STAR CLUSTERS, AND DWARF GALAXIES
Year: 2013

We present a multi wavelength analysis of three compact galaxy groups, Hickson compact groups (HCGs) 16, 22, and 42, which describe a sequence in terms of gas richness, from space (Swift, Hubble Space Telescope (HST) and Spitzer) and ground based (Las Campanas Observatory and Cerro Tololo Inter American Observatory) imaging and spectroscopy. We study various signs of past interactions including a faint, dusty tidal feature about HCG 16A, which we tentatively age date at <1?Gyr. This represents the possible detection of a tidal feature at the end of its phase of optical observability. Our HST images also resolve what were thought to be double nuclei in HCG 16C and D into multiple, distinct sources, likely to be star clusters. Beyond our phenomenological treatment, we focus primarily on contrasting the stellar populations across these three groups. The star clusters show a remarkable intermediate age population in HCG 22, and identify the time at which star formation was quenched in HCG 42. We also search for dwarf galaxies at accordant redshifts. The inclusion of 33 members and 27 associates (possible members) radically changes group dynamical masses, which in turn may affect previous evolutionary classifications. The extended membership paints a picture of relative isolation in HCGs 16 and 22, but shows HCG 42 to be part of a larger structure, following a dichotomy expected from recent studies. We conclude that (1) star cluster populations provide an excellent metric of evolutionary state, as they can age date the past epochs of star formation; and (2) the extended dwarf galaxy population must be considered in assessing the dynamical state of a compact group.
RADIATION HORMESIS, PUBLIC HEALTH, AND PUBLIC POLICY: A COMMENTARY
Year: 2006

Public policy affecting public health regarding effects of low level ionizing radiations has been, and is being, determined by effects estimates based on linear or other monotonic extrapolation from high level radiation dose response data to presumed ecologically realistic low level exposure effects. Such predictive, unmeasured estimates are very possibly in serious error; they are incompatible with observed low level dose response data that indicate a negative correlation between low level radiation data and health effects, such as cancer mortality rates. Observed negative correlations with low level radiation data are to be expected on the basis of evidence supporting the validity of the hormesis phenomenon. Hormesis theory, derived in part from evolutionary biology, asserts that while high levels of exposure to an agent such as ionizing radiation are indeed hazardous, ecologically realistic low levels can be stimulatory and largely beneficial. Stimulation of activities of DNA and other repair mechanisms may be involved. Although evidence of the reality of radiation hormesis has been reported in about 1000 scientific publications over the last century, this effect has been largely unrecognized. Moreover, this widespread non acceptance of hormesis as a real world phenomenon is usually but not always present in the case of chemical hormesis; the oversight appears systematic. The ignoring of the hormesis phenomenon seems to constitute a very serious error in modern biomedical science and in preventive medicine. A mathematical model is offered that describes the general shape of certain doseresponse functions when radiation hormesis at low level exposure is taken into consideration along with the well known detrimental effects of high level radiation.