nils-herrmann/clean_scirepeval huggingface dataset:
On the classification of EEG/HEG-based attention levels via time-frequency selective multilayer perceptron for BCI-based neurofeedback system

Attention Deficit/Hyperactivity Disorder (ADHD) is a neurobehavioral disorder which leads to the difficulty on focusing, paying attention and controlling normal behavior. Globally, the prevalence of ADHD is estimated to be 6.5%. Medicine has been widely used for the treatment of ADHD symptoms, but the patient may have a chance to suffer from the side effects of drug, such as vomit, rash, urticarial, cardiac arrthymia and insomnia. In this paper, we propose the alternative medicine system based on the brain-computer interface (BCI) technology called neurofeedback. The proposed neurofeedback system simultaneously employs two important signals, i.e. electroencephalogram (EEG) and hemoencephalogram (HEG), which can quickly reveal the brain functional network. The treatment criteria are that, for EEG signals, the patient needs to maintain the beta activities (13-30 Hz) while reducing the alpha activities (7-13 Hz). Simultaneously, HEG signals need to be maintained continuously increasing to some setting thresholds of the brain blood oxygenation levels. Time-frequency selective multilayer perceptron (MLP) is employed to capture the mentioned phenomena in real-time. The experimental results show that the proposed system yields the sensitivity of 98.16% and the specificity of 95.57%. Furthermore, from the resulting weights of the proposed MLP, we can also conclude that HEG signals yield the most impact to our neurofeedback treatment followed by the alpha, beta, and theta activities, respectively.
Follicular mucinosis associated with nonlymphoid skin conditions.

Follicular mucinosis coexisting with lymphoproliferative disorders has been thoroughly debated. However, it has been rarely reported in association with inflammatory disorders.



Thirteen cases have been retrieved, and those with cutaneous lymphoma or alopecia mucinosa were excluded.



Follicular mucinosis was found in the setting of squamous cell carcinoma, seborrheic keratosis, simple prurigo, acne vulgaris, dextrometorphan-induced phototoxicity, polymorphous light eruption (2 cases), insect bite (2 cases), tick bite, discoid lupus erythematosus, drug-related vasculitis, and demodecidosis. Unexpectedly, our observations revealed a preponderating accumulation of mucin related to photo-exposed areas, sun-associated dermatoses, and histopathologic solar elastosis. The amount of mucin filling the follicles apparently correlated with the intensity of perifollicular inflammatory infiltrate, which was present in all cases. The concurrence of dermal interstitial mucin was found in 7 cases (54%).



The concurrence of interstitial dermal mucinosis or the potential role of both ultraviolet radiation and the perifollicular inflammatory infiltrates in its pathogenesis deserves further investigations. Precise recognition and understanding of this distinctive, reactive histological pattern may prevent our patients from unnecessary diagnostic and therapeutic strategies.
TQM, innovation and the role of cultural change

The purpose of this paper is to better understand the influence of total quality management (TQM) practices on incremental and radical innovation, examining the role of diverse cultural change as a mediator, particularly in firms where ever-increasing competitive pressure demands a combination of quality and innovation.  From previous research on the influence of TQM practices on innovation, the paper proposes a model which is tested through a survey carried out on a sample of 72 Spanish firms that have been drastically hit by competition from Asian companies, achieving a 51.42 percent valid return rate.  None of the sets of TQM practices directly affects radical innovation, while all of them have a significant and positive relationship with incremental innovation. However, when the paper introduces cultural change as a mediating factor, the model’s goodness of fit improves substantially, and all the relations are significant.  The results reveal the power of cultural change to connect the diversity of the TQM practices and incremental and radical innovation. Further research is needed for a more comprehensive understanding of the role of cultural change in these relationships and to test the model in a longitudinal study.  Managers can use the potential inherent in TQM to stimulate a paradoxical cultural context that favours innovation. This is especially relevant for enhancing radical innovation. Social implications – Given the extent to which TQM has been applied over the last 20 years, the social impact of this study is relevant, particularly in the current environment of economic crisis which calls for an increase in efficiency and innovation, adaptation and change.  The paper introduces a multidimensional analysis of TQM and a broad perspective of innovation. The paper also develops an original definition of cultural change made up of apparently contradicting values, including exploitation and exploration, and introduces it as a mediating variable in the TQM-innovation model.
Spatial frequency masking and Weber's Law

The threshold masking effect of one simultaneously presented grating upon another was studied as a function of mask contrast and frequency. The masking function typically obeys Weber's Law with method-of-adjustment psychophysics, and typically does not with forced-choice. This apparent discrepancy was studied in some detail. We suggest that thresholds can be set with at least two different criteria, depending upon experimental conditions. When the mask is unfamiliar, it functions as noise and detection occurs at a constant signal/noise ratio, which yields Weber's Law. When the mask is highly familiar, its masking effect is less and obeys a power law. This power-law masking appears to represent an inherent non-linearity of the visual system.
Effective Music Feature NCP: Enhancing Cover Song Recognition with Music Transcription

Chroma is a widespread feature for cover song recognition, as it is robust against non-tonal components and independent of timbre and specific instruments. However, Chroma is derived from spectrogram, thus it provides a coarse approximation representation of musical score. In this paper, we proposed a similar but more effective feature Note Class Profile (NCP) derived with music transcription techniques. NCP is a multi-dimensional time serie, each column of which denotes the energy distribution of 12 note classes. Experimental results on benchmark datasets demonstrated its superior performance over existing music features. In addition, NCP feature can be enhanced further with the development of music transcription techniques. The source code can be found in github1.
Empathy Training for Resident Physicians: A Randomized Controlled Trial of a Neuroscience-Informed Curriculum

Physician empathy is an essential attribute of the patient–physician relationship and is associated with better outcomes, greater patient safety and fewer malpractice claims. We tested whether an innovative empathy training protocol grounded in neuroscience could improve physician empathy as rated by patients. Randomized controlled trial. We randomly assigned residents and fellows from surgery, medicine, anesthesiology, psychiatry, ophthalmology, and orthopedics (N = 99, 52% female, mean age 30.6 ± 3.6) to receive standard post-graduate medical education or education augmented with three 60-minute empathy training modules. Patient ratings of physician empathy were assessed within one-month pre-training and between 1–2 months post-training with the use of the Consultation and Relational Empathy (CARE) measure. Each physician was rated by multiple patients (pre-mean = 4.6 ± 3.1; post-mean 4.9 ± 2.5), who were blinded to physician randomization. The primary outcome was change score on the patient-rated CARE. The empathy training group showed greater changes in patient-rated CARE scores than the control (difference 2.2; P = 0.04). Trained physicians also showed greater changes in knowledge of the neurobiology of empathy (difference 1.8; P < 0.001) and in ability to decode facial expressions of emotion (difference 1.9; P < 0.001). A brief intervention grounded in the neurobiology of empathy significantly improved physician empathy as rated by patients, suggesting that the quality of care in medicine could be improved by integrating the neuroscience of empathy into medical education.
A note on Platt’s probabilistic outputs for support vector machines

Platt’s probabilistic outputs for Support Vector Machines (Platt, J. in Smola, A., et al. (eds.) Advances in large margin classifiers. Cambridge, 2000) has been popular for applications that require posterior class probabilities. In this note, we propose an improved algorithm that theoretically converges and avoids numerical difficulties. A simple and ready-to-use pseudo code is included.
A Survey of Modern Object Detection Literature using Deep Learning

Object detection is the identification of an object in the image along with its localization and classification. It has wide spread applications and is a critical component for vision based software systems. This paper seeks to perform a rigorous survey of modern object detection algorithms that use deep learning. As part of the survey, the topics explored include various algorithms, quality metrics, speed/size trade offs and training methodologies. This paper focuses on the two types of object detection algorithmsthe SSD class of single step detectors and the Faster R-CNN class of two step detectors. Techniques to construct detectors that are portable and fast on low powered devices are also addressed by exploring new lightweight convolutional base architectures. Ultimately, a rigorous review of the strengths and weaknesses of each detector leads us to the present state of the art.
Deep Affinity Network for Multiple Object Tracking

Multiple Object Tracking (MOT) plays an important role in solving many fundamental problems in video analysis in computer vision. Most MOT methods employ two steps: Object Detection and Data Association. The first step detects objects of interest in every frame of a video, and the second establishes correspondence between the detected objects in different frames to obtain their tracks. Object detection has made tremendous progress in the last few years due to deep learning. However, data association for tracking still relies on hand crafted constraints such as appearance, motion, spatial proximity, grouping etc. to compute affinities between the objects in different frames. In this paper, we harness the power of deep learning for data association in tracking by jointly modeling object appearances and their affinities between different frames in an end-to-end fashion. The proposed Deep Affinity Network (DAN) learns compact; yet comprehensive features of pre-detected objects at several levels of abstraction, and performs exhaustive pairing permutations of those features in any two frames to infer object affinities. DAN also accounts for multiple objects appearing and disappearing between video frames. We exploit the resulting efficient affinity computations to associate objects in the current frame deep into the previous frames for reliable on-line tracking. Our technique is evaluated on popular multiple object tracking challenges MOT15, MOT17 and UA-DETRAC. Comprehensive benchmarking under twelve evaluation metrics demonstrates that our approach is among the best performing techniques on the leader board for these challenges. The open source implementation of our work is available at https://github.com/shijieS/SST.git.
A coded generalization of selective repeat ARQ

Reducing the in-order delivery, or playback, delay of reliable transport layer protocols over error prone networks can significantly improve application layer performance. This is especially true for applications that have time sensitive constraints such as streaming services. We explore the benefits of a coded generalization of selective repeat ARQ for minimizing the in-order delivery delay. An analysis of the delay's first two moments is provided so that we can determine when and how much redundancy should be added to meet a user's requirements. Numerical results help show the gains over selective repeat ARQ, as well as the trade-offs between meeting the user's delay constraints and the costs inflicted on the achievable rate. Finally, the analysis is compared with experimental results to help illustrate how our work can be used to help inform system decisions.
Involvement of hsr203J like gene homologue, protease and protease inhibitors in triggering differential defense response against Alternaria blight in Brassica

Transcript profiling of hsr203J, a known marker gene for Hypersensitive response (HR), was performed to delineate its role in differential defense against Alternaria brassicae in tolerant and susceptible genotypes of Brassica juncea. Reverse transcriptase (RT) PCR approach was utilized to investigate the correlation between expression of hsr203J like gene(s) and pathogenesis in stage dependent manner. It was revealed that the expression of hsr203J like gene increased as disease progressed from initial too late stage of infection in tolerant genotype. However, in susceptible genotype, its expression increased up to middle stage of infection with no expression in late stage of infection. In both genotypes, no expression of hsr203J like gene was observed in healthy leaves. It was observed that whereas three homologues of hsr203J like gene express at the late stage of infection in tolerant genotype, only single homologue of same expresses in susceptible genotype throughout all stages of infection. This indicates the role of hsr203J homologues in determining the differential defense response against Alternaria blight in Brassica. Determination of specific activity and in-gel assay revealed differential accumulation of protease and protease inhibitor in tolerant and susceptible genotypes at different stages of infection. Induction of differential protease and protease inhibitor activity appears to modulate the cell death during HR response to pathogen. Dissection of pathway leading to HR related cell death will enable us to know the molecular basis of disease resistance which will, in turn, help in engineering Brassica for resistance to Alternaria blight.
Passing , Traveling and Reality : Social Constructionism and the Metaphysics of Race

Among race theorists, the view that race is a social construction is widespread. While the term ‘social construction’ is sometimes intended tomeanmerely that race does not (as once believed) constitute a robust, biological natural kind, it often labels the stronger position that race is real, but not a biological kind. For example, Charles Mills (1998) writes that, ‘‘the task of those working on race is to put race in quotes, ‘race’, while still insisting that nevertheless, it exists (and moves people)’’(xiv, italics his). It is to ‘‘make a plausible social ontology neither essentialist, innate, nor transhistorical, but real enough for all that’’ (xiv). Racial constructionism, thus conceived, is a metaphysical position that contrasts both with the view that race is an important biological kind (racial naturalism) and with the more recent claim that race does not exist (racial skepticism). The desire for a constructionist metaphysics of race emerges against the background of a cluster of normative disputes, including:
SemEval-2014 Task 9: Sentiment Analysis in Twitter

This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a twopoint and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.
Towards High Performance Video Object Detection for Mobiles 3 2 Revisiting Video Object Detection Baseline

Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles. It is also unclear whether the key principles of sparse feature propagation and multi-frame feature aggregation apply at very limited computational resources. In this paper, we present a light weight network architecture for video object detection on mobiles. Light weight image object detector is applied on sparse key frames. A very small network, Light Flow, is designed for establishing correspondence across frames. A flow-guided GRU module is designed to effectively aggregate features on key frames. For non-key frames, sparse feature propagation is performed. The whole network can be trained end-to-end. The proposed system achieves 60.2% mAP score on ImageNet VID validation at speed of 25.6 fps on mobiles (e.g., HuaWei Mate 8).
Deep Paraphrase Detection in Indian Languages

This paper presents an approach to the problem of paraphrase identification in English and Indian languages using Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Traditional machine learning approaches used features that involved using resources such as POS taggers, dependency parsers, etc. for English. The lack of similar resources for Indian languages has been a deterrent to the advancement of paraphrase detection task in Indian languages. Deep learning helps in overcoming the shortcomings of traditional machine Learning techniques. In this paper, three approaches have been proposed, a simple CNN that uses word embeddings as input, a CNN that uses WordNet scores as input and RNN based approach with both LSTM and bi-directional LSTM.
Crime in Urban Areas: : A Data Mining Perspective

Urban safety and security play a crucial role in improving life quality of citizen and the sustainable development of urban. Traditional urban crime research focused on leveraging demographic data, which is insufficient to capture the complexity and dynamics of urban crimes. In the era of big data, we have witnessed advanced ways to collect and integrate fine-grained urban, mobile, and public service data that contains various crime-related sources as well as rich environmental and social information. The availability of big urban data provides unprecedented opportunities, which enable us to conduct advanced urban crime research. Meanwhile, environmental and social crime theories from criminology provide better understandings about the behaviors of offenders and complex patterns of crime in urban. They can not only help bridge the gap from what we have (big urban data) to what we want to understand about urban crime (urban crime analysis); but also guide us to build computational models for crime. In this article, we give an overview to key theories from criminology, summarize crime analysis on urban data, review state-of-the-art algorithms for various types of computational crime tasks and discuss some appealing research directions that can bring the urban crime research into a new frontier.
Youth Participatory Action Research (YPAR) 2.0: how technological innovation and digital organizing sparked a food revolution in East Oakland.

This article argues that technological innovation is transforming the flow of information, the fluidity of social action, and is giving birth to new forms of bottom up innovation that are capable of expanding and exploding old theories of reproduction and resistance because 'smart mobs', 'street knowledge', and 'social movements' cannot be neutralized by powerful structural forces in the same old ways. The purpose of this article is to develop the concept of YPAR 2.0 in which new technologies enable young people to visualize, validate, and transform social inequalities by using local knowledge in innovative ways that deepen civic engagement, democratize data, expand educational opportunity, inform policy, and mobilize community assets. Specifically this article documents how digital technology (including a mobile, mapping and SMS platform called Streetwyze and paper-mapping tool Local Ground) - coupled with 'ground-truthing' - an approach in which community members work with researchers to collect and verify 'public' data - sparked a food revolution in East Oakland that led to an increase in young people's self-esteem, environmental stewardship, academic engagement, and positioned urban youth to become community leaders and community builders who are connected and committed to health and well-being of their neighborhoods. This article provides an overview of how the YPAR 2.0 Model was developed along with recommendations and implications for future research and collaborations between youth, teachers, neighborhood leaders, and youth serving organizations.
Boolean compressed sensing: LP relaxation for group testing

We revisit the well-known problem of boolean group testing which attempts to discover a sparse subset of faulty items in a large set of mostly good items using a small number of pooled (or grouped) tests. This problem originated during the second WorldWar, and has been the subject of active research during the 70's, and 80's. Recently, there has been a resurgence of interest due to the striking parallels between group testing and the now highly popular field of compressed sensing. In fact, boolean group testing is nothing but compressed sensing in a different algebra - with boolean `AND' and `OR' operations replacing vector space multiplication and addition. In this paper we review existing solutions for non-adaptive (batch) group testing and propose a linear programming relaxation solution, which has a resemblance to the basis pursuit algorithm for sparse recovery in linear models. We compare its performance to alternative methods for group testing.
Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders

While recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses. Unlike past work that has focused on diversifying the output of the decoder at word-level to alleviate this problem, we present a novel framework based on conditional variational autoencoders that captures the discourse-level diversity in the encoder. Our model uses latent variables to learn a distribution over potential conversational intents and generates diverse responses using only greedy decoders. We have further developed a novel variant that is integrated with linguistic prior knowledge for better performance. Finally, the training procedure is improved by introducing a bag-of-word loss. Our proposed models have been validated to generate significantly more diverse responses than baseline approaches and exhibit competence in discourse-level decision-making.1