nils-herrmann/clean_scirepeval huggingface dataset:
A Web-based E-learning Platform for Physical Education

The major purpose of this paper is to develop a Web-based E-learning Platform for physical education. The Platform provides sports related courseware which includes physical motions, exercise rules and first-aid treatment. The courseware is represented using digital multimedia materials which include video, 2D animation and 3D virtual reality. Courseware within digital multimedia materials not only can increase the learning efficient but also inspires students’ strong interest in learning, especially in the area of Physical Education. The design concept of our project is based on ADDIE model with the five basic phases of analysis, design, development, implementation, and evaluation. Via the usage of this Web-based E-learning platform, user can learn the relative knowledge about sports at anytime and in everyplace. We hope to let players perform efficient self learning for sports skills, indirectly foster mutual help, cooperation, nice norms of law-abiding via the learning of exercise rules, and become skilled at accurate recreation knowledge and first-aid expertise. Moreover, coaches can use the system as a teaching facility to mitigate loading on teaching.
Visual Servoing from Deep Neural Networks

We present a deep neural network-based method to perform high-precision, robust and real-time 6 DOF visual servoing. The paper describes how to create a dataset simulating various perturbations (occlusions and lighting conditions) from a single real-world image of the scene. A convolutional neural network is fine-tuned using this dataset to estimate the relative pose between two images of the same scene. The output of the network is then employed in a visual servoing control scheme. The method converges robustly even in difficult real-world settings with strong lighting variations and occlusions. A positioning error of less than one millimeter is obtained in experiments with a 6 DOF robot.
A new framework for business impact analysis in business continuity management ( with a case study

Resumption of critical processes/functions after occurrence of any disruptive event is essential from the business continuity (BC) viewpoint. Business impact analysis (BIA) is a key part of a business continuity management system (BCMS) in which an organization’s key products/services along with the critical functions and their BC related indices, i.e., the maximum tolerable period of disruption (MTPD) and the minimum business continuity objective (MBCO) are determined. This study proposes a novel framework to conduct the BIA in organizations in a more systematic and comprehensive way mostly by relying on some effective multi attribute decision making (MADM) techniques. A case study in an auto part manufacturer is also provided to validate the proposed framework whose results demonstrate the applicability and usefulness of the proposed approach.
BSLIC: SLIC Superpixels Based on Boundary Term

A modified method for better superpixel generation based on simple linear iterative clustering (SLIC) is presented and named BSLIC in this paper. By initializing cluster centers in hexagon distribution and performing k-means clustering in a limited region, the generated superpixels are shaped into regular and compact hexagons. The additional cluster centers are initialized as edge pixels to improve boundary adherence, which is further promoted by incorporating the boundary term into the distance calculation of the k-means clustering. Berkeley Segmentation Dataset BSDS500 is used to qualitatively and quantitatively evaluate the proposed BSLIC method. Experimental results show that BSLIC achieves an excellent compromise between boundary adherence and regularity of size and shape. In comparison with SLIC, the boundary adherence of BSLIC is increased by at most 12.43% for boundary recall and 3.51% for under segmentation error.
Wim Hof Method Explained 2 Colofon the Wim Hof Method Explained

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Asperger's disorder and criminal behavior: forensic-psychiatric considerations.

Asperger's Disorder remains an under-diagnosed condition because of clinical unfamiliarity with its adult presentation. As forensic clinicians become familiar with the presentation of Asperger's disorder, it appears that affected individuals are over-represented in forensic criminal settings. Unique features of such persons may heighten their risks for engaging in criminal behavior. Both Theory of Mind deficits and a predilection for intense narrow interests, when coupled with deficient social awareness of salient interpersonal and social constraints on behavior, can result in criminal acts. We discuss comorbidities of forensic relevance. We present several cases that highlight these issues and review the relevant forensic literature. Furthermore, there may be valid questions as to degree of criminal responsibility in such persons. From a neuropsychiatric perspective, these disorders appear to have a biological underpinning for deficits in empathy, a finding that may have important repercussions when assessing remorse in criminal proceedings.
Development of Coupling and Decoupling Network for Surge Immunity Test

In order to simulate the effects of lightning and switching transients on power lines, coupling and decoupling network (CDN) is necessary for performing surge tests on electrical and electronic equipment. This paper presents a particular analysis of the circuit of CDN and design details according to the requirement of the standard IEC 61000-4-5. The CDN circuits are simulated by electromagnetic transients program (EMTP) to judge the suitability of the selected component values and performance of the designed CDN. Simulation results also show that increasing of decoupling capacitance or inductance can reduce the residual surge voltage on the power line inputs of the CDN. Based on the voltage drop control and residual surge voltage limiting, smaller decoupling inductance and relatively larger decoupling capacitance should be chosen if EUT has a higher rated current. The CDN developed according to the simulation results satisfy the design specifications well.
What constitutes “ style ” in authorship attribution ?

Authorship attribution typically uses all information representing both content and style whereas attribution based only on stylistic aspects may be robust in cross-domain settings. This paper analyzes different linguistic aspects that may help represent style. Specifically, we study the role of syntax and lexical words (nouns, verbs, adjectives and adverbs) in representing style. We use a purely syntactic language model to study the significance of sentence structures in both singledomain and cross-domain attribution, i.e. cross-topic and cross-genre attribution. We show that syntax may be helpful for cross-genre attribution while cross-topic attribution and single-domain may benefit from additional lexical information. Further, pure syntactic models may not be effective by themselves and need to be used in combination with other robust models. To study the role of word choice, we perform attribution by masking all words or specific topic words corresponding to nouns, verbs, adjectives and adverbs. Using a single-domain dataset, IMDB1M reviews, we demonstrate the heavy influence of common nouns and proper nouns in attribution, thereby highlighting topic interference. Using cross-domain Guardian10 dataset, we show that some common nouns, verbs, adjectives and adverbs may help with stylometric attribution as demonstrated by masking topic words corresponding to these parts-of-speech. As expected, it was observed that proper nouns are heavily influenced by content and cross-domain attribution will benefit from completely masking them.
Near-duplicate detection for images and videos

In this paper, we describe a system for detecting duplicate images and videos in a large collection of multimedia data. Our system consists of three major elements: Local-Difference-Pattern (LDP) as the unified feature to describe both images and videos, Locality-Sensitive-Hashing (LSH) as the core indexing structure to assure the most frequent data access occurred in the main memory, and multi-steps verification for queries to best exclude false positives and to increase the precision. The experimental results, validated on two public datasets, demonstrate that the proposed method is robust against the common image-processing tricks used to produce duplicates. In addition, the memory requirement has been addressed in our system to handle large-scale database.
Emotion recognition based on physiological signals using valence-arousal model

This paper considers two dimensional valence-arousal model. Pictorial stimuli of International Affective Picture Systems were chosen for emotion elicitation. Physiological signals like, Galvanic Skin Response, Heart Rate, Respiration Rate and Skin Temperature were measured for accessing emotional responses. The experimental procedure uses non-invasive sensors for signal collection. A group of healthy volunteers was shown four types of emotional stimuli categorized as High Valence High Arousal, High Valence Low Arousal, Low Valence High Arousal and Low Valence Low Arousal for around thirty minutes for emotion elicitation. Linear and Quadratic Discriminant Analysis are used and compared to the emotional class classification. Classification of stimuli into one of the four classes has been attempted on the basis of measurements on responses of experimental subjects. If classification is restricted within the responses of a specific individual, the classification results show high accuracy. However, if the problem is extended to entire population, the accuracy drops significantly.
Universal Stem-Loop Primer Method for Screening and Quantification of MicroRNA

RT-qPCR is the accepted technique for the quantification of microRNA (miR) expression: however, stem-loop RT-PCR, the most frequently used method for quantification of miRs, is time- and reagent-consuming as well as inconvenient for scanning. We established a new method called 'universal stem-loop primer' (USLP) with 8 random nucleotides instead of a specific sequence at the 3' end of the traditional stem-loop primer (TSLP), for screening miR profile and to semi-quantify expression of miRs. Peripheral blood samples were cultured with phytohaemagglutinin (PHA), and then 87 candidate miRs were scanned in cultured T cells. By USLP, our study revealed that the expression of miR-150-5p (miR-150) decreased nearly 10-fold, and miR-155-5p (miR-155) increased more than 7-fold after treated with PHA. The results of the dissociation curve and gel electrophoresis showed that the PCR production of the USLP and TSLP were specificity. The USLP method has high precision because of its low ICV (ICV<2.5%). The sensitivity of the USLP is up to 103 copies/µl miR. As compared with the TSLP, USLP saved 75% the cost of primers and 60% of the test time. The USLP method is a simple, rapid, precise, sensitive, and cost-effective approach that is suitable for screening miR profiles.
Ultra-Resolving Face Images by Discriminative Generative Networks

www.PosterPresentations.com Conventional face super-resolution methods, also known as face hallucination, become very fragile when the input low-resolution image size is too small and upscaling factors are large. To address these shortcomings, we present a discriminative generative network that can ultraresolve a very low resolution face image of size 16×16 pixels to its 8x larger version by reconstructing 64 pixels from a single pixel. We introduce a pixel-wise L2 regularization term to the generative model and exploit the feedback of the discriminative network to make the upsampled face images more similar to real ones. In our framework, the discriminative network learns the essential constituent parts of the faces and the generative network blends these parts in the most accurate fashion to the input image. Our method can ultra-resolve a wide range of very low-resolution images directly regardless of pose and facial expression variations. Extensive experimental evaluations demonstrate that the presented ultra-resolution by discriminative generative networks (UR-DGN) achieves more appealing results than the state-of-the-art.
A high-performance non-isolated DC-DC buck converter design based on wide bandgap power devices

Due to the limitations in the operating conditions of the conventional silicon (Si) power device based dc-dc buck converters as well as its notable power losses, there is an increased interest and need to exploit the promising features of wide bandgap power devices toward more efficient converters. This paper presents the design of an efficient, high performance and reliable non-isolated dc-dc buck converter based on silicon carbide (SiC) power devices. First, the converter is designed using the SiC-MOSFET/SiC-Schottky diode power devices and it is compared with the CoolMOS/Si-diode based converter. A comprehensive study is done in terms of high switching frequency capabilities, different junction temperatures, as well as with a wide range of output load currents. The results show that a substantial and advanced amelioration in the efflciency for the designed SiC-MOSFET/SiC-Schottky diode based converter at operating conditions that include high switching frequencies, high junction temperatures, as well as at high and low output load currents.
Twelve tips for facilitating Millennials' learning.

The current, so-called "Millennial" generation of learners is frequently characterized as having deep understanding of, and appreciation for, technology and social connectedness. This generation of learners has also been molded by a unique set of cultural influences that are essential for medical educators to consider in all aspects of their teaching, including curriculum design, student assessment, and interactions between faculty and learners.


The following tips outline an approach to facilitating learning of our current generation of medical trainees.


The method is based on the available literature and the authors' experiences with Millennial Learners in medical training.


The 12 tips provide detailed approaches and specific strategies for understanding and engaging Millennial Learners and enhancing their learning.


With an increased understanding of the characteristics of the current generation of medical trainees, faculty will be better able to facilitate learning and optimize interactions with Millennial Learners.
Dimensions of meaning in the occupations of daily life.

According to the existential philosophers, meaning, purpose and choice are necessary for quality of life. Qualitative researchers exploring the perspectives of people who have experienced health crises have also identified the need for meaning, purpose and choice following life disruptions. Although espousing the importance of meaning in occupation, occupational therapy theory has been primarily preoccupied with purposeful occupations and thus appears inadequate to address issues of meaning within people's lives.



This paper proposes that the fundamental orientation of occupational therapy should be the contributions that occupation makes to meaning in people's lives, furthering the suggestion that occupation might be viewed as comprising dimensions of meaning: doing, being, belonging and becoming. Drawing upon perspectives and research from philosophers, social scientists and occupational therapists, this paper will argue for a renewed understanding of occupation in terms of dimensions of meaning rather than as divisible activities of self-care, productivity and leisure.



Focusing on meaningful, rather than purposeful occupations more closely aligns the profession with its espoused aspiration to enable the enhancement of quality of life.
Tracking control based on neural network strategy for robot manipulator

This study presents a sliding-mode neural-network (SMNN) control system for the tracking control of an n rigid-link robot manipulator to achieve high-precision position control. The aim of this study is to overcome some of the shortcomings of conventional robust controllers such as a model-based adaptive controller requires the system dynamics in detail; the fuzzy rule learning scheme has a latent stability problem; an adaptive control scheme for robot manipulator via fuzzy compensator requires strict constrained conditions and prior system knowledge. In the SMNN control system, a neural network controller is developed to mimic an equivalent control law in the sliding-mode control, and a robust controller is designed to curb the system dynamics on the sliding surface for guaranteeing the asymptotic stability property. Moreover, an adaptive bound estimation algorithm is employed to estimate the upper bound of uncertainties. All adaptive learning algorithms in the SMNN control system are derived from the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system whether the uncertainties occur or not. Computer simulations of a two-link robot manipulator verify the validity of the proposed control strategy in the possible presence of uncertainties and di4erent trajectories. The proposed SMNN control scheme possesses two salient merits: (1) it guarantees the stability of the controlled system, and (2) no constrained conditions and prior knowledge of the controlled plant is required in the design process. This new intelligent methodology provides the designer with an alternative choice to control an n rigid-link robot manipulator.
Influence of marine n-3 polyunsaturated fatty acids on immune function and a systematic review of their effects on clinical outcomes in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease of the joints and bones. The n-6 polyunsaturated fatty acid (PUFA) arachidonic acid (ARA) is the precursor of inflammatory eicosanoids which are involved in RA. Some therapies used in RA target ARA metabolism. Marine n-3 PUFAs (eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA)) found in oily fish and fish oils decrease the ARA content of cells involved in immune responses and decrease the production of inflammatory eicosanoids from ARA. EPA gives rise to eicosanoid mediators that are less inflammatory than those produced from ARA and both EPA and DHA give rise to resolvins that are anti-inflammatory and inflammation resolving, although little is known about these latter mediators in RA. Marine n-3 PUFAs can affect other aspects of immunity and inflammation relevant to RA, including dendritic cell and T cell function and production of inflammatory cytokines and reactive oxygen species, although findings for these outcomes are not consistent. Fish oil has been shown to slow the development of arthritis in animal models and to reduce disease severity. A number of randomised controlled trials of marine n-3 PUFAs have been performed in patients with RA. A systematic review included 23 studies. Evidence is seen for a fairly consistent, but modest, benefit of marine n-3 PUFAs on joint swelling and pain, duration of morning stiffness, global assessments of pain and disease activity, and use of non-steroidal anti-inflammatory drugs.
Data Driven Analysis on the Effect of Online Judge System

Programming, which is a professional basic course of computer science major and software engineering major, has the characteristics of difficult entry and high practical requirements. In view of the problems in the teaching of curriculum experiment, we adopt the online evaluation method to carry on the teaching reform. This paper describes the specific methods and processes of online evaluation in the "program design" experiment teaching, makes an in-depth analysis of the data generated by the online judge system and the student test scores, and then takes a questionnaire survey on the teaching effect. Data driven analysis and survey results show that online judge system can effectively improve students' programming abilities, and also play a positive role in promoting students' enthusiasm and knowledge. Finally, this paper summarizes the existing problems of the online judge system in the experimental teaching process of programming course and gives the countermeasures.
A 0.4–6GHz CMOS tunable load with independent Q tuning for 5G filtering applications

The design of a high impedance, high Q tunable load is presented with operating frequency between 400MHz and close to 6GHz. The bandwidth is made independently tunable of the carrier frequency by using an active inductor resonator with multiple tunable capacitances. The Q factor can be tuned from a value 40 up to 300. The circuit is targeted at 5G wideband applications requiring narrow band filtering where both centre frequency and bandwidth needs to be tunable. The circuit impedance is applied to the output stage of a standard CMOS cascode and results show that high Q factors can be achieved close to 6GHz with 11dB rejection at 20MHz offset from the centre frequency. The circuit architecture takes advantage of currently available low cost, low area tunable capacitors based on micro-electromechanical systems (MEMS) and Barium Strontium Titanate (BST).
Wideband Miniaturized Half Bowtie Printed Dipole Antenna With Integrated Balun for Wireless Applications

A linearly polarized miniaturized printed dipole antenna with novel half bowtie radiating arm is presented for wireless applications including the 2.4 GHz ISM band. This design is approximately 0.363 λ in length at central frequency of 2.97 GHz. An integrated balun with inductive transitions is employed for wideband impedance matching without changing the geometry of radiating arms. This half bowtie dipole antenna displays 47% bandwidth, and a simulated efficiency of over 90% with miniature size. The radiation patterns are largely omnidirectional and display a useful level of measured gain across the impedance bandwidth. The size and performance of the miniaturized half bowtie dipole antenna is compared with similar reduced size antennas with respect to their overall footprint, substrate dielectric constant, frequency of operation and impedance bandwidth. This half bowtie design in this communication outperforms the reference antennas in virtually all categories.
The deep kernelized autoencoder

Autoencoders learn data representations (codes) in such a way that the input is reproduced at the output of the network. However, it is not always clear what kind of properties of the input data need to be captured by the codes. Kernel machines have experienced great success by operating via inner-products in a theoretically well-defined reproducing kernel Hilbert space, hence capturing topological properties of input data. In this paper, we enhance the autoencoder’s ability to learn effective data representations by aligning inner products between codes with respect to a kernel matrix. By doing so, the proposed kernelized autoencoder allows learning similarity-preserving embeddings of input data, where the notion of similarity is explicitly controlled by the user and encoded in a positive semi-definite kernel matrix. Experiments are performed for evaluating both reconstruction and kernel alignment performance in classification tasks and visualization of high-dimensional data. Additionally, we show that our method is capable to emulate kernel principal component analysis on a denoising task, obtaining competitive results at a much lower computational cost.