nils-herrmann/clean_scirepeval huggingface dataset:
Classification Techniques used in Speech Recognition Applications : A Review

Classification phase is one of the most active research and application areas of speech recognition. The literature is vast and growing. This paper summarizes the some of the most important developments in the classification procedures of the speech recognition applications. The state of art of the classification technique has also been presented in this paper. Different classification techniques and their parameter estimation methods, properties, advantages, disadvantages along with their application areas are discussed with each classification method. Our purpose is to provide a synthesis of the published research in the area of speech recognition and stimulate further research interests and efforts in the identified topics. This paper presents an overview of several pattern classification methods available in literature for speech recognition applications.  Classification, Classifiers, Taxonomy, Bayes decision theory, Acoustic Phonetic approach, Template matching, Dynamic Time Warping(DTW),Vector Quantization(VQ), Hidden Markov Model(HMM), Artificial Neural Network(ANN), Support Vector Machine(SVM), KNearest Neighbor(KNN), Gaussian Mixture Modeling, Clustering techniques, Evaluations, Applications.
Education at University and Industry 4.0

Modern students will work in the “Industry 4.0” and create digital economy of Russia. Digital economy is based on the infrastructure organization of production, which is based on the network interaction of production and technology. The world infrastructure of transport, trading, finance provides a technological organization of production and consumers. Industry 4.0 begins with the creation of redundant infrastructure networks - Industrial Internet of Things (IIoT). Today, university education corresponds to the processing form of the technological organization. The infrastructure form of the organization of educational process is necessary. Information technologies supporting the entire training process and representing it on the Internet, do not exist. A new methodology of educational process is suggested. Training in the training process, lectures, seminars, laboratory works are organized according to the logical fulfillment of the target works. The task of the teacher is the design of the target works and the analysis of the result. The simulation training system is a form of the activity organization that is directed to the development of participants and obtaining a result. The result will be a course work as a part of the project in the simulation system. Students act in specially developed problem, training and developing situations. Practical formation in the field of wireless communications technologies is carried out on the basis of equipment of software defined radio (SDR) or universal software radio peripheral (USRP) systems as a combination of hardware and software platforms for creating prototypes of real radio systems. Laboratory and research works are performed on the basis of this firmware radio system (SDR) in the remote Internet access Mode. This conforms to the principles of the Industry4.0. The project is carried out under the control of the teacher. The activity of each student is monitored. The Internet provides individual activities and simulation, communication, scheduling, and group activity analysis. As a result, there is no knowledge of the sum of knowledge, and the integral technical culture, which is a criterion of the education.
Allergic contact cheilitis caused by menthol in toothpaste and throat medication: a case report.

A 29-year-old man, suffering from dry skin, who had a brother with rhinoconjunctivitis, was referred to us by a private dermatologist in January 2016 because of erythematous and scaly lips, which had been almost continuously present for >8 years. The eruption had started at both corners of the mouth, but gradually spread to the entire upper and lower lips. At consultation, we also observed erythema and scales at the philtrum, arranged in two vertical lines divided from each other at the midline
A Study of BER-Optimal ADC-Based Receiver for Serial Links

Analog-to-digital converter (ADC)-based multi-Gb/s serial link receivers have gained increasing attention in the backplane community due to the desire for higher I/O throughput, ease of design portability, and flexibility. However, the power dissipation in such receivers is dominated by the ADC. ADCs in serial links employ signal-to-noise-and-distortion ratio (SNDR) and effective-number-of-bit (ENOB) as performance metrics as these are the standard for generic ADC design. This paper studies the use of information-based metrics such as bit-error-rate (BER) to design a BER-optimal ADC (BOA) for serial links. Channel parameters such as the m-clustering value and the threshold non-uniformity metric ht are introduced and employed to quantify the BER improvement achieved by a BOA over a conventional uniform ADC (CUA) in a receiver. Analytical expressions for BER improvement are derived and validated through simulations. A prototype BOA is designed, fabricated and tested in a 1.2 V, 90 nm LP CMOS process to verify the results of this study. BOA's variable-threshold and variable-resolution configurations are implemented via an 8-bit single-core, multiple-output passive digital-to-analog converter (DAC), which incurs an additional power overhead of <; 0.1% (approximately 50 μW). Measurement results show examples in which the BER achieved by the 3-bit BOA receiver is lower by a factor of 109 and 1010, as compared to the 4-bit and 3-bit CUA receivers, respectively, at a data rate of 4-Gb/s and a transmitted signal amplitude of 180 mVppd.
Compositional Learning for Human Object Interaction

The world of human-object interactions is rich. While generally we sit on chairs and sofas, if need be we can even sit on TVs or top of shelves. In recent years, there has been progress in modeling actions and human-object interactions. However, most of these approaches require lots of data. It is not clear if the learned representations of actions are generalizable to new categories. In this paper, we explore the problem of zero-shot learning of human-object interactions. Given limited verb-noun interactions in training data, we want to learn a model than can work even on unseen combinations. To deal with this problem, In this paper, we propose a novel method using external knowledge graph and graph convolutional networks which learns how to compose classifiers for verbnoun pairs. We also provide benchmarks on several dataset for zero-shot learning including both image and video. We hope our method, dataset and baselines will facilitate future research in this direction.
Photonuclear reactions triggered by lightning discharge

Lightning and thunderclouds are natural particle accelerators. Avalanches of relativistic runaway electrons, which develop in electric fields within thunderclouds, emit bremsstrahlung γ-rays. These γ-rays have been detected by ground-based observatories, by airborne detectors and as terrestrial γ-ray flashes from space. The energy of the γ-rays is sufficiently high that they can trigger atmospheric photonuclear reactions that produce neutrons and eventually positrons via β+ decay of the unstable radioactive isotopes, most notably 13N, which is generated via 14N + γ → 13N + n, where γ denotes a photon and n a neutron. However, this reaction has hitherto not been observed conclusively, despite increasing observational evidence of neutrons and positrons that are presumably derived from such reactions. Here we report ground-based observations of neutron and positron signals after lightning. During a thunderstorm on 6 February 2017 in Japan, a γ-ray flash with a duration of less than one millisecond was detected at our monitoring sites 0.5–1.7 kilometres away from the lightning. The subsequent γ-ray afterglow subsided quickly, with an exponential decay constant of 40–60 milliseconds, and was followed by prolonged line emission at about 0.511 megaelectronvolts, which lasted for a minute. The observed decay timescale and spectral cutoff at about 10 megaelectronvolts of the γ-ray afterglow are well explained by de-excitation γ-rays from nuclei excited by neutron capture. The centre energy of the prolonged line emission corresponds to electron–positron annihilation, providing conclusive evidence of positrons being produced after the lightning.
The effectiveness of psychodynamic psychotherapies: An update.

This paper provides a comprehensive review of outcome studies and meta-analyses of effectiveness studies of psychodynamic therapy (PDT) for the major categories of mental disorders. Comparisons with inactive controls (waitlist, treatment as usual and placebo) generally but by no means invariably show PDT to be effective for depression, some anxiety disorders, eating disorders and somatic disorders. There is little evidence to support its implementation for post-traumatic stress disorder, obsessive-compulsive disorder, bulimia nervosa, cocaine dependence or psychosis. The strongest current evidence base supports relatively long-term psychodynamic treatment of some personality disorders, particularly borderline personality disorder. Comparisons with active treatments rarely identify PDT as superior to control interventions and studies are generally not appropriately designed to provide tests of statistical equivalence. Studies that demonstrate inferiority of PDT to alternatives exist, but are small in number and often questionable in design. Reviews of the field appear to be subject to allegiance effects. The present review recommends abandoning the inherently conservative strategy of comparing heterogeneous "families" of therapies for heterogeneous diagnostic groups. Instead, it advocates using the opportunities provided by bioscience and computational psychiatry to creatively explore and assess the value of protocol-directed combinations of specific treatment components to address the key problems of individual patients.
Deformable Part-based Fully Convolutional Network for Object Detection

Existing region-based object detectors are limited to regions with fixed box geometry to represent objects, even if those are highly non-rectangular. In this paper we introduce DP-FCN, a deep model for object detection which explicitly adapts to shapes of objects with deformable parts. Without additional annotations, it learns to focus on discriminative elements and to align them, and simultaneously brings more invariance for classification and geometric information to refine localization. DP-FCN is composed of three main modules: a Fully Convolutional Network to efficiently maintain spatial resolution, a deformable part-based RoI pooling layer to optimize positions of parts and build invariance, and a deformation-aware localization module explicitly exploiting displacements of parts to improve accuracy of bounding box regression. We experimentally validate our model and show significant gains. DP-FCN achieves state-of-the-art performances of 83.1% and 80.9% on PASCAL VOC 2007 and 2012 with VOC data only.
A technique for computer detection and correction of spelling errors

The method described assumes that a word which cannot be found in a dictionary has at most one error, which might be a wrong, missing or extra letter or a single transposition. The unidentified input word is compared to the dictionary again, testing each time to see if the words match—assuming one of these errors occurred. During a test run on garbled text, correct identifications were made for over 95 percent of these error types.
INTERNET DATING ADS : SEX , ETHNICITY , AGE-RELATED DIFFERENCES , AND SUPPORT FOR EVOLUTIONARY THEORY

The purpose of this research project was to investigate possible sex, ethnicity, and age group differences involving the information placed in Internet dating ads, and to contrast the findings with predictions from evolutionary theory (e.g., women being more selective than men) and with findings from previous studies involving heterosexual dating ads placed in newspapers and magazines. Of particular interest were the types and number of characteristics sought in a dating partner. Results generally supported predictions from evolutionary theory. Women listed more desired characteristics for a partner than did men. Women focused more on non-physical attributes such as ambition and character than did men, and men focused more on youth and attractiveness than did women. There was; however, considerable similarity in terms of the five most desired attributes listed by both men and women. Women listed the following desired characteristics in men most often: humor, honesty, caring, openness, and personality. Men desired the following: affection, humor, honesty, openness, and attractive women. These desired characteristics were also significantly different from those found in recent studies which looked at dating ads placed in newspapers.
Work-related stress, education and work ability among hospital nurses.

This paper is a report of a study conducted to determine which occupational stressors are present in nurses' working environment; to describe and compare occupational stress between two educational groups of nurses; to estimate which stressors and to what extent predict nurses' work ability; and to determine if educational level predicts nurses' work ability.



Nurses' occupational stress adversely affects their health and nursing quality. Higher educational level has been shown to have positive effects on the preservation of good work ability.



A cross-sectional study was conducted in 2006-2007. Questionnaires were distributed to a convenience sample of 1392 (59%) nurses employed at four university hospitals in Croatia (n = 2364). The response rate was 78% (n = 1086). Data were collected using the Occupational Stress Assessment Questionnaire and Work Ability Index Questionnaire.



We identified six major groups of occupational stressors: 'Organization of work and financial issues', 'public criticism', 'hazards at workplace', 'interpersonal conflicts at workplace', 'shift work' and 'professional and intellectual demands'. Nurses with secondary school qualifications perceived Hazards at workplace and Shift work as statistically significantly more stressful than nurses a with college degree. Predictors statistically significantly related with low work ability were: Organization of work and financial issues (odds ratio = 1.69, 95% confidence interval 122-236), lower educational level (odds ratio = 1.69, 95% confidence interval 122-236) and older age (odds ratio = 1.07, 95% confidence interval 1.05-1.09).



Hospital managers should develop strategies to address and improve the quality of working conditions for nurses in Croatian hospitals. Providing educational and career prospects can contribute to decreasing nurses' occupational stress levels, thus maintaining their work ability.
Intensive lowering of LDL cholesterol with 80 mg versus 20 mg simvastatin daily in 12 064 survivors of myocardial infarction: a double-blind randomised trial

Lowering of LDL cholesterol reduces major vascular events, but whether more intensive therapy safely produces extra benefits is uncertain. We aimed to establish efficacy and safety of more intensive statin treatment in patients at high cardiovascular risk.



We undertook a double-blind randomised trial in 12,064 men and women aged 18-80 years with a history of myocardial infarction. Participants were either currently on or had clear indication for statin therapy, and had a total cholesterol concentration of at least 3·5 mmol/L if already on a statin or 4·5 mmol/L if not. Randomisation to either 80 mg or 20 mg simvastatin daily was done centrally using a minimisation algorithm. Participants were assessed at 2, 4, 8, and 12 months after randomisation and then every 6 months until final follow-up. The primary endpoint was major vascular events, defined as coronary death, myocardial infarction, stroke, or arterial revascularisation. Analysis was by intention to treat. 
6031 participants were allocated 80 mg simvastatin daily, and 6033 allocated 20 mg simvastatin daily. During a mean follow-up of 6·7 (SD 1·5) years, allocation to 80 mg simvastatin produced an average 0·35 (SE 0·01) mmol/L greater reduction in LDL cholesterol compared with allocation to 20 mg. Major vascular events occurred in 1477 (24·5%) participants allocated 80 mg simvastatin versus 1553 (25·7%) of those allocated 20 mg, corresponding to a 6% proportional reduction (risk ratio 0·94, 95% CI 0·88-1·01; p=0·10). There were no apparent differences in numbers of haemorrhagic strokes (24 [0·4%] vs 25 [0·4%]) or deaths attributed to vascular (565 [9·4%] vs 572 [9·5%]) or non-vascular (399 [6·6%] vs 398 [6·6%]) causes. Compared with two (0·03%) cases of myopathy in patients taking 20 mg simvastatin daily, there were 53 (0·9%) cases in the 80 mg group.



The 6% (SE 3·5%) reduction in major vascular events with a further 0·35 mmol/L reduction in LDL cholesterol in our trial is consistent with previous trials. Myopathy was increased with 80 mg simvastatin daily, but intensive lowering of LDL cholesterol can be achieved safely with other regimens.


 Council and the British Heart Foundation.
An Empirical Evaluation of Deep Learning for ICD-9 Code Assignment using MIMIC-III Clinical Notes

Code assignment is important on many levels in the modern hospital, from ensuring accurate billing process to creating a valid record of patient care history. However, the coding process is tedious, subjective, and requires medical coders with extensive training. The objective of this study is to evaluate the performance of deep learning based systems to automatically map clinical notes to medical codes. We applied the state-of-the-art deep learning methods such as Recurrent Neural Networks and Convolution Neural Networks on MIMIC-III dataset. Experiments show that the deep-learning-based methods outperform other conventional machine learning methods. Our evaluations are focused on end-to-end learning methods without manually defined rules. From our evaluations, the best models are able to predict the top 10 ICD-9 codes with 69.57% F1 and 89.67% accuracy; the top 10 ICD-9 categories with 72.33% F1 and 85.88% accuracy. The evaluation tools and resources are available at https://github.com/lsy3/ clinical-notes-diagnosis-dl-nlp.
Classifier and Exemplar Synthesis for Zero-Shot Learning

Zero-shot learning (ZSL) enables solving a task without the need to see its examples. In this paper, we propose two ZSL frameworks that learn to synthesize parameters for novel unseen classes. First, we propose to cast the problem of ZSL as learning manifold embeddings from graphs composed of object classes, leading to a flexible approach that synthesizes “classifiers” for the unseen classes. Then, we define an auxiliary task of synthesizing “exemplars” for the unseen classes to be used as an automatic denoising mechanism for any existing ZSL approaches or as an effective ZSL model by itself. On five visual recognition benchmark datasets, we demonstrate the superior performances of our proposed frameworks in various scenarios of both conventional and generalized ZSL. Finally, we provide valuable insights through a series of empirical analyses, among which are a comparison of semantic representations on the full ImageNet benchmark as well as a comparison of metrics used in generalized ZSL. Our code and data are publicly available at https: //github.com/pujols/Zero-shot-learning-journal. Soravit Changpinyo Google AI E-mail: schangpi@google.com Wei-Lun Chao Cornell University, Department of Computer Science E-mail: weilunchao760414@gmail.com Boqing Gong Tencent AI Lab E-mail: boqinggo@outlook.com Fei Sha University of Southern California, Department of Computer Science E-mail: feisha@usc.edu
Teaching UAVs to Race Using UE4Sim

Automating the navigation of unmanned aerial vehicles (UAVs) in diverse scenarios has gained much attention in the recent years. However, teaching UAVs to fly in challenging environments remains an unsolved problem, mainly due to the lack of data for training. In this paper, we develop a photo-realistic simulator that can afford the generation of large amounts of training data (both images rendered from the UAV camera and its controls) to teach a UAV to autonomously race through challenging tracks. We train a deep neural network to predict UAV controls from raw image data for the task of autonomous UAV racing. Training is done through imitation learning enabled by data augmentation to allow for the correction of navigation mistakes. Extensive experiments demonstrate that our trained network (when sufficient data augmentation is used) outperforms state-of-the-art methods and flies more consistently than many human pilots.
A novel escapable social interaction test reveals that social behavior and mPFC activation during an escapable social encounter are altered by post-weaning social isolation and are dependent on the aggressiveness of the stimulus rat

Post-weaning social isolation (PSI) has been shown to increase aggressive behavior and alter medial prefrontal cortex (mPFC) function in social species such as rats. Here we developed a novel escapable social interaction test (ESIT) allowing for the quantification of escape and social behaviors in addition to mPFC activation in response to an aggressive or nonaggressive stimulus rat. Male rats were exposed to 3 weeks of PSI (ISO) or group (GRP) housing, and exposed to 3 trials, with either no trial, all trials, or the last trial only with a stimulus rat. Analysis of social behaviors indicated that ISO rats spent less time in the escape chamber and more time engaged in social interaction, aggressive grooming, and boxing than did GRP rats. Interestingly, during the third trial all rats engaged in more of the quantified social behaviors and spent less time escaping in response to aggressive but not nonaggressive stimulus rats. Rats exposed to nonaggressive stimulus rats on the third trial had greater c-fos and ARC immunoreactivity in the mPFC than those exposed to an aggressive stimulus rat. Conversely, a social encounter produced an increase in large PSD-95 punctae in the mPFC independently of trial number, but only in ISO rats exposed to an aggressive stimulus rat. The results presented here demonstrate that PSI increases interaction time and aggressive behaviors during escapable social interaction, and that the aggressiveness of the stimulus rat in a social encounter is an important component of behavioral and neural outcomes for both isolation and group-reared rats.
Integrating LTE-D2D and VLC techniques to support V2V communication

In recent years, with the rapid development of Device-to-Device communication (D2D) underlaying cellular network, numerous researchers propose to use LTE-D2D technique for Vehicle to Vehicle (V2V) communications (LTE-V2V), so as to compensate for the disadvantage of Dedicated Short Range Communication (DSRC) technology. Although it may have some effects, Some problems still existed. The main constrain is the interference between cellular links and LTE-V2V links. Focusing on this question, we propose a hybrid architecture which integrates LTE-V2V and Visible Light Communication based V2V communication (VLC-V2V) to improve system performance. Based on the hybrid system, we develop a novel distributed resource allocation algorithm to reduce the interference between cellular links and LTE-V2V links as well as to make full use of cellular frequency spectrum. Finally, the performance of the proposed system is simulated on MATLAB, and compared with a typical LTE-V2V system under different situations on a urban road. The simulation results prove that the hybrid system can significantly improve the system performance especially in the dense area.