nils-herrmann/clean_scirepeval huggingface dataset:
Computational approaches to fMRI analysis

Analysis methods in cognitive neuroscience have not always matched the richness of fMRI data. Early methods focused on estimating neural activity within individual voxels or regions, averaged over trials or blocks and modeled separately in each participant. This approach mostly neglected the distributed nature of neural representations over voxels, the continuous dynamics of neural activity during tasks, the statistical benefits of performing joint inference over multiple participants and the value of using predictive models to constrain analysis. Several recent exploratory and theory-driven methods have begun to pursue these opportunities. These methods highlight the importance of computational techniques in fMRI analysis, especially machine learning, algorithmic optimization and parallel computing. Adoption of these techniques is enabling a new generation of experiments and analyses that could transform our understanding of some of the most complex—and distinctly human—signals in the brain: acts of cognition such as thoughts, intentions and memories.
Automatic Bengali number plate reader

In this paper, we propose connected component analysis based algorithm to automatically detect and read Bengali number plates used in the metropolitan cities of Bangladesh. Our proposed automatic number plate recognition (ANPR) system involves image pre-processing and morphological operation followed by edge detection, regional localization and character segmentation to identify the Bengali characters in the number plate efficiently as well as with less computational complexity. For various weather conditions, our proposed algorithm shows a detection accuracy rate of ∼95% with an average processing time of 0.75 seconds. Our system might be highly effective in real time traffic control, security enhancement as well in electronic toll collection.
Ethnopharmacological uses, phytochemistry, biological activities, and biotechnological applications of Eclipta prostrata

Eclipta prostrata belongs to a family of medicinal plants (Asteraceae) and plays a role in the treatment of several diseases, including infectious hepatitis, snake venom poisoning, gastritis, and respiratory diseases such as a cough and asthma. A number of compounds, including thiophene derivatives, steroids, triterpenes, flavonoids, polyacetylenes, polypeptides, and coumestans, have been isolated from E. prostrata. The plant functional compounds can act as reducing agent in the field of nanoparticle synthesis. The extracts of E. prostrata are widely used for green biosynthesis of various metal and metal oxide nanoparticles, nanoparticles, which showed a potential for pharmaceutical, biotechnological, and biomedical applications. Establishment of a efficient in vitro regeneration and genetic transformation method of E. prostrata is a vital prerequisite for application of biotechnology in order to improve secondary metabolite yields. The present mini-review discusses its pharmacological profile, chemical constituents, biotechnological, and ethnomedical uses, mainly focusing on antimyotoxic, antihemorrhagic, antiproliferative, antioxidant, antitumor, antihyperglycemic, antidementia, antimicrobial, antihyperlipidemic, antivenom, anti-HIV, and larvicidal activities, so that the pharmaceutical potential of the plant can be better evaluated. The mini review, providing up-to-date phytochemical and other information on E. prostrata, will serve a reference for further studies.
A 4-DOF MEMS Energy Harvester Using Ultrasonic Excitation

This paper presents a microelectromechanical systems (MEMS)-based energy harvester that is designed to harvest electrical energy from an external source of ultrasonic waves. The harvester features a novel 4-degree-of-freedom (DOF) mechanical design that uses three translational resonance modes and one rotational mode, with electrostatic transducers being implemented to perform the conversion of mechanical to electrical energy. The 4-DOF design of the system allows energy to be harvested regardless of the device's orientation, which holds particular benefits for potential applications that include the powering of implanted biomedical devices. Full characterization of the fabricated MEMS harvester is performed, which shows that the device is able to generate electrical power outputs of 180, 189, and 49.1 nW due to ultrasonic excitation from the x-, y-, and z-directions, respectively.
The BeiHang Keystroke Dynamics Authentication System

Keystroke Dynamics is an important biometric solution for person authentication. Based upon keystroke dynamics, this paper designs an embedded password protection device, develops an online system, collects two public databases for promoting the research on keystroke authentication, exploits the Gabor filter bank to characterize keystroke dynamics, and provides benchmark results of three popular classification algorithms, one-class support vector machine, Gaussian classifier, and nearest neighbour classifier.
Information Warfare: Its Application in Military and Civilian Contexts

The lexicon of information warfare (IW), or cyberwar, to use a common variant, has been around for more than two decades (Adams, 1998), but for most of that time it has remained the preserve of the defense community. Within the cloistered world of the Pentagon and its satellite communities, a radically new concept of postindustrial warfare has crystallized, one that is designed to ensure continued U.S. military dominance in the post-Cold War era. There exists an extensive literature on information warfare theory and practice, much of which is in the public domain (De Landa, 1991; Libicki, 1995; Schwartau, 1996; Denning, 1999). Predictably, the prevailing language, images, and metaphors are classically militaristic in character, blurring the fact that many of the underpinning principles and assumptions have application well beyond conventional military contexts. This privileging of military thinking is myopic. Information warfare concepts deserve to be liberated from their military associations and introduced into other discourse communities concerned with understanding the social consequences of pervasive computing. Already, the principles and practices of information warfare are being exhibited, more or less wittingly, in a variety of civilian contexts (from computer-based fraud to cyberstalking), and there are good grounds for assuming that this trend will intensify, causing potentially serious social problems and creating novel challenges for the criminal justice system. To paraphrase a well-worn cliché, information warfare is too important to be left to the military.
Factors Influencing Loss of Body Weight in Cord Blood Transplantation with Nutritional Support for Hematopoietic Stem Cell Transplantation

Patients may experience nutrition-related adverse events during hematopoietic stem cell transplantation (HSCT) and long-term nutritional intervention was likely needed after cord blood transplantation (CBT) for HSCT due to the long engraftment period. Previous studies have examined nutritional therapy for HSCT but there has been no discussion of loss of body weight (LBW) for CBT patients on HSCT nutritional pathways or of the specific nutritional interventions used. The objective of this study was to identify the specific factors influencing LBW in this patient population and to evaluate the nutritional intervention implemented.  Subjects in this retrospective exploratory pilot study were 15 patients who underwent CBT with nutritional support following the HSCT nutritional pathway at the Department of Stem Cell Transplantation, Shizuoka Cancer Center, between 2008 and 2012. Correlations were assessed using Pearson’s product-moment correlation coefficients between percent loss of body weight (%LBW) and bioelectrical impedance analysis results, nutrient intake and graft-versus-host disease (GVHD) as well as between continuous oral intake percentage and nutrition-related adverse events (severity score) and orally ingested calories. Spearman rank-order correlation was used to analyze severity scores. Data was also analyzed using JMP.  Body weight and skeletal muscle mass decreased significantly during follow-up. Percent LBW was correlated with percent loss of skeletal muscle mass (r = 0.89, p<0.001), total nutrient intake (calories: r = 0.69, p<0.001 and protein: r = 0.69, p = 0.02), orally ingested intake (calories: r = 0.53, p = 0.04 and protein: r = 0.60, p = 0.01) and intestinal acute GVHD (r = 0.63, p = 0.01). Continuous oral intake percentage was correlated with nutrition-related adverse events (severity score: r = 0.66, p<0.001) and orally ingested calories (r = !0.55, p<0.001).  Nutritional complications are unavoidable factors influencing LBW in CBT patients on the HSCT nutritional pathway and early nutritional intervention is required. To attenuate LBW in these patients, the intensity of nutritional intervention should be adjusted based on severity scores for nutrition-related adverse events.
Recognizing Eating from Body-Worn Sensors: Combining Free-living and Laboratory Data

Automated dietary monitoring solutions that can find when, what, and how much individuals consume are needed for many applications such as providing feedback to individuals with chronic disease. Advances in body-worn sensors have led to systems with high accuracy for finding meals and even which foods are consumed in each bite. However, most tests are done in controlled lab settings with restricted meal choices, little background noise, and subjects focused on eating. For these systems to be adopted by users it is critical that they work well in realistic situations and be able to handle confounding factors such as background noise, shared meals, and multi-tasking. Work in realistic environments usually has lower accuracy, but has challenges in determining ground truth. Most critically, there has been a significant gap between lab and free-living environments. This is compounded by data usually being collected for different individuals in each setting, making it difficult to determine how the accuracy gap can be closed. We present a multi-modality study on eating recognition, using body-worn motion (head, wrists) and audio (earbud microphone) sensors for 12 participants (6 from the lab study, 6 new to test generalizability). In contrast to the lab, where audio alone has the highest accuracy, we find now that a combination of sensing modalities (audio, motion) is needed; yet sensor placement (head vs. wrist) is not critical. We further find that lab data does generalize to other participants, but while personal free-living data improves accuracy, more data from others can actually lead to worse performance.
Morphological Segmentation with Window LSTM Neural Networks

Morphological segmentation, which aims to break words into meaning-bearing morphemes, is an important task in natural language processing. Most previous work relies heavily on linguistic preprocessing. In this paper, we instead propose novel neural network architectures that learn the structure of input sequences directly from raw input words and are subsequently able to predict morphological boundaries. Our architectures rely on Long Short Term Memory (LSTM) units to accomplish this, but exploit windows of characters to capture more contextual information. Experiments on multiple languages confirm the effectiveness of our models on this task.
Cybersecurity, women and minorities: findings and recommendations from a preliminary investigation

This paper presents the work done by the ACM ITiCSE, 2013 Conference Working Group (WG) on Cybersecurity, Women and Minorities: How to Succeed in the Career! The ITiCSE 2013 conference was held July 1-3, 2013, in Canterbury, United Kingdom. The overall goal of the WG was to conduct a preliminary investigation into the reasons behind the lack of women and minorities within the field of Cybersecurity. This is not just an issue of academic or research interest, but is important in ensuring that a greater number of women and minorities progress through a full career in Cybersecurity. There are currently no statistics available on the numbers of women and minorities either currently enrolled in or graduated from these programs. There is a need to explore the full range of factors that influence women and minority's decisions not to consider a career in Cybersecurity.
Advances in interior permanent magnet (IPM) motor drives

In recent years, high efficiency electric motors are demanded to meet the challenges of energy hungry world with limited fossil fuel sources. Interior permanent magnet (IPM) motors have been increasingly developed and widely used to meet the high efficiency and performance requirements. It is because; an IPM is a highly efficient synchronous motor with built-in advantageous features. This is due to advances in high-energy permanent magnets, smart inverters and intelligent controllers. This paper provides an outline of a modern IPM motor in an electronic world. On-line and soft starting of an IPM motor are provided. Highlights of IPM motors include applications in Japanese air conditioners and hybrid electric vehicles, which are two examples of ac motor drives in domestic heat pumps and passenger automobiles for the competitive market place
Microstrip patch antenna array at 3.8 GHz for WiMax and UAV applications

This paper presents the design of a rectangular microstrip line-fed patch antenna array with a centre frequency of 3.8 GHz for WiMAX and Unmanned Air Vehicle (UAV) applications. A single element, 1×2 and 2×2 microstrip rectangular patch antennas were designed and simulated in Computer Simulation Tool (CST) Microwave Studio environment. The results of designed antennas were compared in terms of Return Loss (S11 parameters), bandwidth, directivity, gain and radiation pattern. Compared to traditional microstrip antennas the proposed array structure achieved a gain and directivity of 13.2 dB and 13.5 dBi respectively. The antenna was fabricated using Rogers Duroid RT-5880 substrate with a dielectric constant er of 2.2 and a thickness of 1.574 mm respectively. The array antennas were measured in the laboratory using Vector Network Analyser (VNA) and the results show good agreement with the array antenna simulation.
Localization & Segmentation of Indian Car Number Plate system : A Projection based Multistage Approach

Enormous incorporation of information technologies into all aspects of recent life caused demand for processing vehicles as conceptual resources in information systems. This can be achieved by a human agent, or by special intelligent equipment which is be able to recognize vehicles by their number plates in a real environment and reflect it into conceptual resources. Because of this, various recognition techniques have been developed and number plate recognition systems are today used in various traffic and security applications, such as parking, access and border control, or tracking of stolen cars. This work deals with problematic from field of artificial intelligence, machine vision and neural networks in construction of an automatic car number plate Recognition (ACNPR). This techniques deals with enhanced shadow removal algorithm, along with horizontal and vertical projection method for number plate localization & segmentation..Work comparatively deals with methods achieving invariance of systems towards image illumination, translations and various light conditions during the capture.
Off-line Identification With Handwritten Signature Images: Survey and Perspectives

The first part of this paper presents a survey of the literature on automatic handwritten signature verification systems using binary or graylevel images, and focuses primarily on preprocessing techniques, feature or primitive extraction methods, comparison processes, and performance evaluation. With these previous studies in mind, we propose, in the second part of this paper, an image-understanding system based on the extraction of a novel representation of handwritten signature images. This approach is text insensitive. A structural match between a reference primitive set Pr and a test primitive set Pt takes into account the geometric shape and spatial relations between primitives. Finally, the local comparison of gray levels between pairs of primitives next to each node of the static solution path N results in a pseudo dynamic similarity measure iJd(Pr, Pt). This scheme allows the elimination, with a certain degree of success, of skilled forgeries such as tracings and photocopies, showing marked gray-level dissimilarity along the signature line.
PALSAR Radiometric and Geometric Calibration

This paper summarizes the results obtained from geometric and radiometric calibrations of the Phased-Array L-Band Synthetic Aperture Radar (PALSAR) on the Advanced Land Observing Satellite, which has been in space for three years. All of the imaging modes of the PALSAR, i.e., single, dual, and full polarimetric strip modes and scanning synthetic aperture radar (SCANSAR), were calibrated and validated using a total of 572 calibration points collected worldwide and distributed targets selected primarily from the Amazon forest. Through raw-data characterization, antenna-pattern estimation using the distributed target data, and polarimetric calibration using the Faraday rotation-free area in the Amazon, we performed the PALSAR radiometric and geometric calibrations and confirmed that the geometric accuracy of the strip mode is 9.7-m root mean square (rms), the geometric accuracy of SCANSAR is 70 m, and the radiometric accuracy is 0.76 dB from a corner-reflector analysis and 0.22 dB from the Amazon data analysis (standard deviation). Polarimetric calibration was successful, resulting in a VV/HH amplitude balance of 1.013 (0.0561 dB) with a standard deviation of 0.062 and a phase balance of  with a standard deviation of 2.66deg .
Design optimization of interior permanent magnet (IPM) motors with maximized torque output in the entire speed range

A multiobjective optimization procedure for the design of interior PM motors based on differential evolution algorithm is presented. A comparative analysis has been carried out for the optimized 5 kW, 50 kW and 200 kW IPM motors with two and three layers of cavities in the rotor to show design trade-offs between goals to minimize the motor volume while maximizing the power output in the field weakening regime with the back emf constraint as the main limiting factor in the design. The finite element method has been used to calculate the motor parameters during the optimization process. The effectiveness of the proposed design methodology has been shown on a 1.65 kW IPM motor with two layers of cavities in the rotor for which a prototype has been built and tested
Why healthcare workers don't wash their hands: a behavioral explanation.

To elucidate behavioral determinants of handwashing among nurses.



Statistical modeling using the Theory of Planned Behavior and relevant components to handwashing behavior by nurses that were derived from focus-group discussions and literature review.



The community and 3 tertiary care hospitals.



Children aged 9-10 years, mothers, and nurses.



Responses from 754 nurses were analyzed using backward linear regression for handwashing intention. We reasoned that handwashing results in 2 distinct behavioral practices--inherent handwashing and elective handwashing--with our model explaining 64% and 76%, respectively, of the variance in behavioral intention. Translation of community handwashing behavior to healthcare settings is the predominant driver of all handwashing, both inherent (weighted beta =2.92) and elective (weighted beta =4.1). Intended elective in-hospital handwashing behavior is further significantly predicted by nurses' beliefs in the benefits of the activity (weighted beta =3.12), peer pressure of senior physicians (weighted beta =3.0) and administrators (weighted beta =2.2), and role modeling (weighted beta =3.0) but only to a minimal extent by reduction in effort (weighted beta =1.13). Inherent community behavior (weighted beta =2.92), attitudes (weighted beta =0.84), and peer behavior (weighted beta =1.08) were strongly predictive of inherent handwashing intent.



A small increase in handwashing adherence may be seen after implementing the use of alcoholic hand rubs, to decrease the effort required to wash hands. However, the facilitation of compliance is not simply related to effort but is highly dependent on altering behavioral perceptions. Thus, introduction of hand rub alone without an associated behavioral modification program is unlikely to induce a sustained increase in hand hygiene compliance.
Customer Churn Prediction Based on SVM-RFE

As markets become increasingly saturated, churn prediction and management has become of great concern to many industries. A company wishing to retain its customers needs to be able to predict those who are likely to churn and will make those customers the focus of customer retention efforts. Today Customer data has properties of large samples, high dimensions and more noises. In response to the limitations of existing feature selection in churn-prediction, we introduce and experimentally evaluate Support vector machine-recursive feature elimination attribute selection algorithm. It can identify key attributes of customer churn, rule out the related and redundant attributes, and reduce the dimensions of data. It is more important that this algorithm is related with the followed classification learning algorithm, so it can be better integrated in churn prediction. The empirical evaluation results suggest that the proposed feature selection algorithm extracts less key attributes and exhibits better satisfactory predictive effectiveness than other three comparable attribute selection algorithms.
Gaussian mixture models with Wasserstein distance

Generative models with both discrete and continuous latent variables are highly motivated by the structure of many real-world data sets. They present, however, subtleties in training often manifesting in the discrete latent being under leveraged. In this paper, we show that such models are more amenable to training when using the Optimal Transport framework of Wasserstein Autoencoders. We find our discrete latent variable to be fully leveraged by the model when trained, without any modifications to the objective function or significant fine tuning. Our model generates comparable samples to other approaches while using relatively simple neural networks, since the discrete latent variable carries much of the descriptive burden. Furthermore, the discrete latent provides significant control over generation.
A Survey of Regularization Methods for Deep Neural Network

Mimicking the human psyche has been a core challenge in machine learning research. Deep Neural network inspired from the human Visual cortex system are powerful computational model represents the large features in a hierarchical way. Overfitting is a major problem in deep learning due to the presence of a large number of features. Dropout is a proficient and simple method to prevent co-adaptation of features and thus stymie to over fit. It simply drops hidden units with probability 0.5.Maxout a new activation unit built on dropout has improved accuracy on datasets. Other recent companions are DropConect, DropAll and stochastic pooling. Dropout achieved state-of-art results on many labelled benchmark datasets: MNIST, CIFAR-10, CIFAR-100 and SVHN. This paper reviews different techniques to reduce Overfitting in Neural Network.  Deep neural networks, Regularization, Overfitting, Supervised learning learning,