nils-herrmann/clean_scirepeval huggingface dataset:
Increased frontal and paralimbic activation following ayahuasca, the pan-amazonian inebriant

Ayahuasca is a South American psychoactive plant tea which contains the serotonergic psychedelic N,N-dimethyltryptamine (DMT) and monoamine-oxidase inhibitors that render DMT orally active. Previous investigations with ayahuasca have highlighted a psychotropic effect profile characterized by enhanced introspective attention, with individuals reporting altered somatic perceptions and intense emotional modifications, frequently accompanied by visual imagery. Despite recent advances in the study of ayahuasca pharmacology, the neural correlates of acute ayahuasca intoxication remain largely unknown. To investigate the effects of ayahuasca administration on regional cerebral blood flow. Fifteen male volunteers with prior experience in the use of psychedelics received a single oral dose of encapsulated freeze-dried ayahuasca equivalent to 1.0 mg DMT/kg body weight and a placebo in a randomized double-blind clinical trial. Regional cerebral blood flow was measured 100–110 min after drug administration by means of single photon emission tomography (SPECT). Ayahuasca administration led to significant activation of frontal and paralimbic brain regions. Increased blood perfusion was observed bilaterally in the anterior insula, with greater intensity in the right hemisphere, and in the anterior cingulate/frontomedial cortex of the right hemisphere, areas previously implicated in somatic awareness, subjective feeling states, and emotional arousal. Additional increases were observed in the left amygdala/parahippocampal gyrus, a structure also involved in emotional arousal. The present results suggest that ayahuasca interacts with neural systems that are central to interoception and emotional processing and point to a modulatory role of serotonergic neurotransmission in these processes.
Turning telecommunications call details to churn prediction: a data mining approach

As deregulation, stew technologies, and new competitors open up the mobile telecommunications industry, churn prediction and management has become of great concern to mobile service providers: A mobile service provider wishing to retain its subscribers needs to be able to predict which of them may be at-risk of changing services and will make those subscribers the focus of customer retention efforts. In response to the limitations of existing churn-prediction systems and the unavailability of customer demographics in the mobile telecommunications provider investigated, we propose, design, and experimentally evaluate a churn-prediction technique that predicts churning from subscriber contractual information and call pattern changes extracted from call details. This proposed technique is capable of identifying potential churners at the contract level for a specific prediction time-period. In addition, the proposed technique incorporates the multi-classifier class-combiner approach to address the challenge of a highly skewed class distribution between churners and non-churners. The empirical evaluation results suggest that the proposed call-behavior-based churn-prediction technique exhibits satisfactory predictive effectiveness when more recent call details are employed for the churn prediction model construction. Furthermore, the proposed technique is able to demonstrate satisfactory or reasonable predictive power within the one-month interval between model construction and churn prediction. Using a previous demographics-based churn-prediction system as a reference, the lift factors attained by our proposed technique appear largely satisfactory.
Network Intrusion Detection System using attack behavior classification

Intrusion Detection Systems (IDS) have become a necessity in computer security systems because of the increase in unauthorized accesses and attacks. Intrusion Detection is a major component in computer security systems that can be classified as Host-based Intrusion Detection System (HIDS), which protects a certain host or system and Network-based Intrusion detection system (NIDS), which protects a network of hosts and systems. This paper addresses Probes attacks or reconnaissance attacks, which try to collect any possible relevant information in the network. Network probe attacks have two types: Host Sweep and Port Scan attacks. Host Sweep attacks determine the hosts that exist in the network, while port scan attacks determine the available services that exist in the network. This paper uses an intelligent system to maximize the recognition rate of network attacks by embedding the temporal behavior of the attacks into a TDNN neural network structure. The proposed system consists of five modules: packet capture engine, preprocessor, pattern recognition, classification, and monitoring and alert module. We have tested the system in a real environment where it has shown good capability in detecting attacks. In addition, the system has been tested using DARPA 1998 dataset with 100% recognition rate. In fact, our system can recognize attacks in a constant time.
Low operating voltage and short settling time CMOS charge pump for MEMS applications

A 16-stage constant-threshold-voltage type charge pump was designed and fabricated in a standard CMOS 0.18-pm process. With an input clock voltage of less than 1 2 V , the maximum possible output voltage is 14.8 V, with a 65 psec rise time. The charge pump employs metal-insulator-metal (MIM) capacitors in order to minimize the chip area (0.8 x 0.9 mm’). This circuit is intended for generating on-chip actuation voltages for microelectro-mechanical devices and systems (MEMS).
MACA: a modified author co-citation analysis method combined with general descriptive metadata of citations

Author co-citation analysis (ACA) is a well-known and frequently-used method to exhibit the academic researchers and the professional field sketch according to co-citation relationships between authors in an article set. However, visualizing subtle examination is limited because only author co-citation information is required in ACA. The proposed method, called modified author co-citation analysis (MACA), exploits author co-citation relationship, citations published time, citations published carriers, and citations keywords, to construct MACA-based co-citation matrices. According to the results of our experiments: (1) MACA shows a good clustering result with more delicacy and more clearness; (2) more information involved in co-citation analysis performs good visual acuity; (3) in visualization of co-citation network produced by MACA, the points in different categories have far more distance, and the points indicating authors in the same category are closer together. As a result, the proposed MACA is found that more detailed and subtle information of a knowledge domain analyzed can be obtained, compared to ACA.
Comparative study of a new dermal filler Uma Jeunesse® and Juvéderm®.

Innovation in technology has resulted in the emergence of better, longer-lasting hyaluronic acid implants with fewer side effects. The new dermal implant Uma Jeunesse® was compared to Juvéderm® in this split-face study.



Uma Jeunesse® is crosslinked with butanediol diglycidyl ether (BDDE) using a new crosslinking technology. Uma Jeunesse® and Juvéderm® Ultra 3 were injected in a split-face study on 17 healthy volunteers, whose ages ranged from 33-58 years. There were 14 women and three men with medium to deep nasolabial folds. All subjects randomly received either Uma Jeunesse® or Juvéderm®) Ultra 3 on one half of their face. Patients were followed up for 9 months.



Juvéderm® was easier to inject with lesser injection pain because of lidocaine, but late postinjection pain was much less with Uma Jeunesse® as compared to Juvéderm®. Overall rate of early and late complications as well as adverse events was lower with Uma Jeunesse® than Juvéderm(®) . After 9 months of follow-up, Uma Jeunesse® lasted in tissues for longer as compared to Juvéderm(®) even in patients injected for the first time (P<0.0001). Patient acceptability rate of Uma Jeunesse® was also much higher. Perception of pain during injection was lesser with Juvéderm® probably because of the presence of lidocaine.



The new dermal implant Uma Jeunesse® is a safe and patient-friendly product which resides in the tissues for longer with maintenance of aesthetic effect over and beyond 6 months, reaching 9 months in over 80% of patients, and Juvéderm® injection is less painful.
HCI and Autonomous Vehicles: Contextual Experience Informs Design

The interaction between drivers and their cars will change significantly with the introduction of autonomous vehicles. The driver's role will shift towards a supervisory control of their autonomous vehicle. The eventual relief from the driving task enables a complete new area of research and practice in human-computer interaction and interaction design. In this one-day workshop, participants will explore the opportunities the design space of autonomous driving will bring to HCI researchers and designers. On the day before workshop participants are invited to visit (together with workshop organizers) Google Partnerplex and Stanford University. At Google participants will have the opportunity to explore Google's autonomous car simulator and might have the chance to experience one of the Google Cars (if available). At Stanford participants are invited to ride in a Wizard-of-Oz autonomous vehicle. Based on this first-hand experience we will discuss design approaches and prototype interaction systems during the next day's workshop. The outcome of this workshop will be a set of concepts, interaction sketches, and low-fidelity paper prototypes that address constraints and potentials of driving in an autonomous car.
A Millimeter-Wave (23–32 GHz) Wideband BiCMOS Low-Noise Amplifier

This paper presents a 23-32 GHz wideband BiCMOS low-noise amplifier (LNA). The LNA utilizes coupled-resonators to provide a wideband load. To our knowledge, the proposed LNA achieves the widest bandwidth with minimum power consumption using 0.18 ¿m BiCMOS technology in K-band. Analytical expressions for the wideband input matching, gain, noise figure and linearity are presented. The LNA is implemented using 0.18 ¿m BiCMOS technology and occupies an area of 0.25 mm2 . It achieves a voltage gain of 12 dB, 3-dB bandwidth of 9 GHz, noise figure between 4.5-6.3 dB, linearity higher than -6.4 dBm with a power consumption of 13 mW from a 1.5 V supply.
A High-Performance Drive Circuit for All Solid-State Marx Generator

In recent years, all solid-state Marx generators have been found more and more extensive applications in industrial, environmental, and biological fields. All solid-state Marx generators have many requirements for their drive circuits, such as good synchrony for driving signals at different stages, fine isolation between control signals and the main circuits, adjustable pulsewidths and frequencies, and good driving abilities. This paper proposes a high-performance drive circuit for a 24-stage Marx generator based on IGBTs. A half-bridge circuit using IR2110 outputs a high-current turning-on drive signal (positive) and a turning-off drive signal (negative) with adjustable dead time. The control drivers are input to the common primary side of 24 nanocrystalline magnetic transformers, which isolate the control circuit and the main circuit. Through gate circuits at the second sides of the magnetic cores, the turning-on drive signal charges 24 gate-emitter capacitors to required voltages and consequently all IGBTs move into on state until the turning-off signal arrives. Similarly, the negative turning-off drive signal charges all gate-emitter capacitors to a negative voltage which ensures all IGBTs stay in the off state. Therefore, the pulsewidth is determined by the phase difference between the turning-on and turning-off drive signals. Equipped with this drive circuit, the 24-stage Marx generator is able to produce stable high voltage pulse with a peak value of -9.6 kV, PRF $0.05\sim 5$ kHz, and different pulsewidths. In this paper, the design details and experimental confirmation of the proposed drive circuit are illustrated.
An Analysis of Chorus Features in Popular Song

This paper presents a computational study of the perceptual and musicological audio features that correlate with the structural function of sections in pop songs, specifically the chorus. Choruses have been described as more prominent, more catchy and more memorable than other sections in a song, yet chorus detection applications have always been primarily based on identifying the most-repeated section in a song. Inspired by cognitive research rather than applied signal processing, this computational analysis compiles a list of robust and interpretable features and models their influence on the ‘chorusness’ of a collection of song sections from the Billboard dataset. This is done through the unsupervised learning of a probabilistic graphical model. We show that timbre and timbre variety are more strongly related to chorus qualities than harmony and absolute pitch height. A regression and a classification experiment are performed to quantify these relations.
Background modelling based on generative unet

Modelling is a crucial step in background/foreground detection which could be used in video analysis, such as surveillance, people counting, face detection and pose estimation. Most methods need to choose the hyper parameters manually or use ground truth background masks (GT). In this work, we present an unsupervised deep background (BG) modelling method called BM-Unet which is based on a generative architecture that given a certain frame as input it generates as output the corresponding background image — to be more precise, a probabilistic heat map of the colour values. Our method learns parameters automatically and an augmented version of it that utilises colour, intensity differences and optical flow between a reference and a target frame is robust to rapid illumination changes and camera jitter. Besides, it can be used on a new video sequence without the need of ground truth background/foreground masks for training. Experiment evaluations on challenging sequences in SBMnet data set demonstrate promising results over state-of-the-art methods.
Discrete Connection and Covariant Derivative for Vector Field Analysis and Design

In this article, we introduce a discrete definition of connection on simplicial manifolds, involving closed-form continuous expressions within simplices and finite rotations across simplices. The finite-dimensional parameters of this connection are optimally computed by minimizing a quadratic measure of the deviation to the (discontinuous) Levi-Civita connection induced by the embedding of the input triangle mesh, or to any metric connection with arbitrary cone singularities at vertices. From this discrete connection, a covariant derivative is constructed through exact differentiation, leading to explicit expressions for local integrals of first-order derivatives (such as divergence, curl, and the Cauchy-Riemann operator) and for L2-based energies (such as the Dirichlet energy). We finally demonstrate the utility, flexibility, and accuracy of our discrete formulations for the design and analysis of vector, n-vector, and n-direction fields.
Chemical crystal identification with deep learning machine vision

This study was carried out with the purpose of testing the ability of deep learning machine vision to identify microscopic objects and geometries found in chemical crystal structures. A database of 6994 images taken with a light microscope showing microscopic crystal details of selected chemical compounds along with 180 images of an unknown chemical was created to train and test, respectively the deep learning models. The models used were GoogLeNet (22 layers deep network) and VGG-16 (16 layers deep network), based on the Caffe framework (University of California, Berkeley, CA) of the DIGITS platform (NVIDIA Corporation, Santa Clara, CA). The two models were successfully trained with the images, having validation accuracy values of 97.38% and 99.65% respectively. Finally, both models were able to correctly identify the unknown chemical sample with a high probability score of 93.34% (GoogLeNet) and 99.41% (VGG-16). The positive results found in this study can be further applied to other unknown sample identification tasks using light microscopy coupled with deep learning machine vision.
Agile Methods and User-Centered Design: How These Two Methodologies are Being Successfully Integrated in Industry

A core principle of agile development is to satisfy the customer by providing valuable software on an early and continuous basis. For a software application to be valuable it should have a user interface that is usable. Recently there has been some evidence that suggests using agile methods alone does not ensure that an applications UI is usable. As a result, there is currently interest in combining Agile methods with user-centered design (UCD) practices. To support existing empirical evidence that these methodologies co-exist effectively we have conducted a study with participants that have previously combined these two methodologies. Our findings, combined with existing work show that the existing model used for agile UCD integration can be broadened into a more common model. In this paper we describe three different approaches taken by our participants to achieve this integration. We term these approaches the generalist, specialist, and the hybrid approach.
AutoComment: Mining question and answer sites for automatic comment generation

Code comments improve software maintainability. To address the comment scarcity issue, we propose a new automatic comment generation approach, which mines comments from a large programming Question and Answer (Q&A) site. Q&A sites allow programmers to post questions and receive solutions, which contain code segments together with their descriptions, referred to as code-description mappings.We develop AutoComment to extract such mappings, and leverage them to generate description comments automatically for similar code segments matched in open-source projects. We apply AutoComment to analyze Java and Android tagged Q&A posts to extract 132,767 code-description mappings, which help AutoComment to generate 102 comments automatically for 23 Java and Android projects. The user study results show that the majority of the participants consider the generated comments accurate, adequate, concise, and useful in helping them understand the code.
Early Breast Cancer Detection using Mammogram Images: A Review of Image Processing Techniques

Breast cancer is one of the most common cancers worldwide among women so that one in eight women is affected by this disease during their lifetime. Mammography is the most effective imaging modality for early detection of breast cancer in early stages. Because of poor contrast and low visibility in the mammographic images, early detection of breast cancer is a significant step to efficient treatment of the disease. Different computeraided detection algorithms have been developed to help radiologists provide an accurate diagnosis. This paper reviews the most common image processing approaches developed for detection of masses and calcifications. The main focus of this review is on image segmentation methods and the variables used for early breast cancer detection. Texture analysis is the crucial step in any image segmentation techniques which are based on a local spatial variation of intensity or color. Therefore, various methods of texture analysis for mass and micro-calcification detection in mammography are discussed in details.
Gender and Tenure Diversity in GitHub Teams

Software development is usually a collaborative venture. Open Source Software (OSS) projects are no exception; indeed, by design, the OSS approach can accommodate teams that are more open, geographically distributed, and dynamic than commercial teams. This, we find, leads to OSS teams that are quite diverse. Team diversity, predominantly in offline groups, is known to correlate with team output, mostly with positive effects. How about in OSS? Using GitHub, the largest publicly available collection of OSS projects, we studied how gender and tenure diversity relate to team productivity and turnover. Using regression modeling of GitHub data and the results of a survey, we show that both gender and tenure diversity are positive and significant predictors of productivity, together explaining a sizable fraction of the data variability. These results can inform decision making on all levels, leading to better outcomes in recruiting and performance.
Two-Stream Neural Networks for Tampered Face Detection

We propose a two-stream network for face tampering detection. We train GoogLeNet to detect tampering artifacts in a face classification stream, and train a patch based triplet network to leverage features capturing local noise residuals and camera characteristics as a second stream. In addition, we use two different online face swaping applications to create a new dataset that consists of 2010 tampered images, each of which contains a tampered face. We evaluate the proposed two-stream network on our newly collected dataset. Experimental results demonstrate the effectness of our method.
Mobile computing acceptance factors in the healthcare industry: A structural equation model

This paper presents a revised technology acceptance model to examine what determines mobile healthcare systems (MHS) acceptance by healthcare professionals.



Conformation factor analysis was performed to test the reliability and validity of the measurement model. The structural equation modeling technique was used to evaluate the causal model.



The results indicated that compatibility, perceived usefulness and perceived ease of use significantly affected healthcare professional behavioral intent. MHS self-efficacy had strong indirect impact on healthcare professional behavioral intent through the mediators of perceived usefulness and perceived ease of use. Yet, the hypotheses for technical support and training effects on the perceived usefulness and perceived ease of use were not supported.



This paper provides initial insights into factors that are likely to be significant antecedents of planning and implementing mobile healthcare to enhance professionals' MHS acceptance. The proposed model variables explained 70% of the variance in behavioral intention to use MHS; further study is needed to explore extra significant antecedents of new IT/IS acceptance for mobile healthcare. Such as privacy and security issue, system and information quality, limitations of mobile devices; the above may be other interesting factors for implementing mobile healthcare and could be conducted by qualitative research.
Person Re-Identification by Localizing Discriminative Regions

Person re-identification is a challenging task of matching a person’s image across multiple images captured from different camera views. Recently, deep learning based approaches have been proposed that show promising performance on this task. However, most of these approaches use whole image features to compute the similarity between images. This is not very intuitive since not all the regions in an image contain information about the person identity. In this paper, we introduce an end-to-end Siamese convolutional neural network that firstly localizes discriminative salient image regions and then computes the similarity based on these image regions in conjunction with the whole image. We use Spatial Transformer Networks (STN) for localizing salient regions. Extensive experiments on CUHK01 and CUHK03 datasets show that our method achieves the state-of-the-art performance.