nils-herrmann/clean_scirepeval huggingface dataset:
A web crawler design for data mining

The content of the web has increasingly become a focus for academic research. Computer programs are needed in order to conduct any large-scale processing of web pages, requiring the use of a web crawler at some stage in order to fetch the pages to be analysed. The processing of the text of web pages in order to extract information can be expensive in terms of processor time. Consequently a distributed design is proposed in order to effectively use idle computing resources and to help information scientists avoid the need to employ dedicated equipment. A system developed using the model is examined and the advantages and limitations of the approach are discussed.
Motivation to learn: an overview of contemporary theories

To succinctly summarise five contemporary theories about motivation to learn, articulate key intersections and distinctions among these theories, and identify important considerations for future research.



Motivation has been defined as the process whereby goal-directed activities are initiated and sustained. In expectancy-value theory, motivation is a function of the expectation of success and perceived value. Attribution theory focuses on the causal attributions learners create to explain the results of an activity, and classifies these in terms of their locus, stability and controllability. Social- cognitive theory emphasises self-efficacy as the primary driver of motivated action, and also identifies cues that influence future self-efficacy and support self-regulated learning. Goal orientation theory suggests that learners tend to engage in tasks with concerns about mastering the content (mastery goal, arising from a 'growth' mindset regarding intelligence and learning) or about doing better than others or avoiding failure (performance goals, arising from a 'fixed' mindset). Finally, self-determination theory proposes that optimal performance results from actions motivated by intrinsic interests or by extrinsic values that have become integrated and internalised. Satisfying basic psychosocial needs of autonomy, competence and relatedness promotes such motivation. Looking across all five theories, we note recurrent themes of competence, value, attributions, and interactions between individuals and the learning context.



To avoid conceptual confusion, and perhaps more importantly to maximise the theory-building potential of their work, researchers must be careful (and precise) in how they define, operationalise and measure different motivational constructs. We suggest that motivation research continue to build theory and extend it to health professions domains, identify key outcomes and outcome measures, and test practical educational applications of the principles thus derived.
Validity of PostureScreen Mobile ® in the Measurement of Standing Posture

Validity of PostureScreen Mobile® in the Measurement of Standing Posture Breanna Cristine Berry Hopkins Department of Exercise Sciences, BYU Master of  PostureScreen Mobile® is an app created to quickly screen posture using front and side-view photographs. There is currently a lack of evidence that establishes PostureScreen Mobile® (PSM) as a valid measure of posture. Therefore, the purpose of this preliminary study was to document the validity and reliability of PostureScreen Mobile® in assessing static standing posture.  This study was an experimental trial in which the posture of 50 male participants was assessed a total of six times using two different methods: PostureScreen Mobile® and Vicon 3D motion analysis system (VIC). Postural deviations, as measured during six trials of PSM assessments (3 trials with and 3 trials without anatomical markers), were compared to the postural deviations as measured using the VIC as the criterion measure. Measurement of lateral displacement on the x-axis (shift) and rotation on the y-axis (tilt) were made of the head, shoulders, and hips in the frontal plane. Measurement of forward/rearward displacement on the Z-axis (shift) of the head, shoulders, hips, and knees were made in the sagittal plane. Validity was evaluated by comparing the PSM measurements of shift and tilt of each body part to that of the VIC. Reliability was evaluated by comparing the variance of PSM measurements to the variance of VIC measurements. The statistical model employed the Bayesian framework and consisted of the scaled product of the likelihood of the data given the parameters and prior probability densities for each of the parameters.  PSM tended to overestimate VIC postural tilt and shift measurements in the frontal plane and underestimate VIC postural shift measurements in the sagittal plane. Use of anatomical markers did not universally improve postural measurements with PSM, and in most cases, the variance of postural measurements using PSM exceeded that of VIC. The patterns in the intraclass correlation coefficients (ICC) suggest high trial-to-trial variation in posture.  We conclude that until research further establishes the validity and reliability of the PSM app, it should not be used in research or clinical applications when accurate postural assessments are necessary or when serial measurements of posture will be performed. We suggest that the PSM be used by health and fitness professionals as a screening tool, as described by the manufacturer. Due to the suspected trial-to-trial variation in posture, we question the usefulness of a single postural assessment.
Extrinsic Rewards and Intrinsic Motivation in Education : Reconsidered Once Again

All teachers acknowledge appropriate student behaviors and desired social skills and provide differential attention/response to inappropriate behaviors.  Research demonstrates that teachers who establish an orderly and positive classroom environment by teaching and reinforcing rules and routines reduce behavior problems. Teacher's acknowledgement of appropriate behavior is related to both initial and long-term academic engagement and social success (Akin-Little et al. (2004); Cameron et al.(2001). Rewards (such as approval, praise, recognition, special privileges, points, or other incentives) are most effective in reinforcing students' appropriate behavior when teachers:  Use small rewards frequently, rather than large rewards infrequently;  Deliver rewards quickly after the desired behavior is exhibited;  Reward behavior, not the individual, and communicate to students the specific behavior that led to the reward;  Use several different kinds of rewards selected carefully to ensure that they are reinforcing positive behavior; and  Gradually begin to reduce and then eliminate rewards. Research also shows that the amount of praise that students receive for appropriate behavior should exceed the amount of times they are corrected or reprimanded by a ratio of four to one to improve student academic and behavioral outcomes.  Deci, Koestner, and Ryan (2001) conducted a meta-analysis in which they examined the effect of extrinsic rewards on intrinsic motivation. They found that verbal rewards can enhance intrinsic motivation; however, verbal rewards are less likely to have a positive effect for children than for older individuals (i.e., college students). Verbal rewards can have a negative effect on intrinsic motivation if they are administered in a controlling rather than informational way. When presenting high-level interest tasks, the use of tangible rewards can have negative consequences for subsequent interest, persistence and preference for challenge, especially for children.  There is compelling meta-analytic evidence that appropriate disciplinary interventions including teacher reaction to students appropriate and inappropriate behavior produce positive change in student behavior. Simple and often subtle teacher reactions have been shown to decrease student misbehavior including eye contact, moving closer to the student, a shake of the head, a simple verbal reminder-ideally as privately and subtly as possible, reminder of the desired appropriate behavior, and simply telling the student to stop the inappropriate behavior (Madsen, Becker, & Thomas, 1968). Teachers should also quietly and privately acknowledge appropriate behavior.
Predictive Active Steering Control for Autonomous Vehicle Systems

In this paper, a model predictive control (MPC) approach for controlling an active front steering system in an autonomous vehicle is presented. At each time step, a trajectory is assumed to be known over a finite horizon, and an MPC controller computes the front steering angle in order to follow the trajectory on slippery roads at the highest possible entry speed. We present two approaches with different computational complexities. In the first approach, we formulate the MPC problem by using a nonlinear vehicle model. The second approach is based on successive online linearization of the vehicle model. Discussions on computational complexity and performance of the two schemes are presented. The effectiveness of the proposed MPC formulation is demonstrated by simulation and experimental tests up to 21 m/s on icy roads
Top–Down Fabrication of Gate-All-Around Vertically Stacked Silicon Nanowire FETs With Controllable Polarity

As the current MOSFET scaling trend is facing strong limitations, technologies exploiting novel degrees of freedom at physical and architecture level are promising candidates to enable the continuation of Moore's predictions. In this paper, we report on the fabrication of novel ambipolar Silicon nanowire (SiNW) Schottky-barrier (SB) FET transistors featuring two independent gate-all-around electrodes and vertically stacked SiNW channels. A top-down approach was employed for the nanowire fabrication, using an e-beam lithography defined design pattern. In these transistors, one gate electrode enables the dynamic configuration of the device polarity (n - or p-type) by electrostatic doping of the channel in proximity of the source and drain SBs. The other gate electrode, acting on the center region of the channel switches ON or OFF the device. Measurement results on silicon show Ion/Ioff >106 and subthreshold slopes approaching the thermal limit, ≈ 64 mV/dec (70 mV/dec) for p(n)-type operation in the same physical device. Finally, we show that the XOR logic operation is embedded in the device characteristic, and we demonstrate for the first time a fully functional two-transistor XOR gate.
Creation of a Digital Business Model Builder

Digitization forces industries to adapt to changing market conditions and consumer behavior. Exponential advances in technology, increased consumer power and sharpened competition imply that companies are facing the menace of commoditization. To sustainably succeed in the market, obsolete business models have to be adapted and new business models can be developed. Differentiation and unique selling propositions through innovation as well as holistic stakeholder engagement help companies to master the transformation. To enable companies and start-ups facing the implications of digital change, a tool was created and designed specifically for this demand: the Business Model Builder. This paper investigates the process of transforming the Business Model Builder into a software-supported digitized version. The digital twin allows companies to simulate the iterative adjustment of business models to constantly changing market conditions as well as customer needs on an ongoing basis. The user can modify individual variables, understand interdependencies and see the impact on the result of the business case, i.e. earnings before interest and taxes (EBIT) or economic value added (EVA). The simulation of a business models accordingly provides the opportunity to generate a dynamic view of the business model where any changes of input variables are considered in the result, the business case. Thus, functionality, feasibility and profitability of a business model can be reviewed, tested and validated in the digital simulation tool.
A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation

Recently, neural machine translation (NMT) has been extended to multilinguality, that is to handle more than one translation direction with a single system. Multilingual NMT showed competitive performance against pure bilingual systems. Notably, in low-resource settings, it proved to work effectively and efficiently, thanks to shared representation space that is forced across languages and induces a sort of transfer-learning. Furthermore, multilingual NMT enables so-called zero-shot inference across language pairs never seen at training time. Despite the increasing interest in this framework, an in-depth analysis of what a multilingual NMT model is capable of and what it is not is still missing. Motivated by this, our work (i) provides a quantitative and comparative analysis of the translations produced by bilingual, multilingual and zero-shot systems; (ii) investigates the translation quality of two of the currently dominant neural architectures in MT, which are the Recurrent and the Transformer ones; and (iii) quantitatively explores how the closeness between languages influences the zero-shot translation. Our analysis leverages multiple professional post-edits of automatic translations by several different systems and focuses both on automatic standard metrics (BLEU and TER) and on widely used error categories, which are lexical, morphology, and word order errors.
Additive Margin Softmax for Face Verification

In this letter, we propose a conceptually simple and intuitive learning objective function, i.e., additive margin softmax, for face verification. In general, face verification tasks can be viewed as metric learning problems, even though lots of face verification models are trained in classification schemes. It is possible when a large-margin strategy is introduced into the classification model to encourage intraclass variance minimization. As one alternative, angular softmax has been proposed to incorporate the margin. In this letter, we introduce another kind of margin to the softmax loss function, which is more intuitive and interpretable. Experiments on LFW and MegaFace show that our algorithm performs better when the evaluation criteria are designed for very low false alarm rate.
NetSpam: A Network-Based Spam Detection Framework for Reviews in Online Social Media

Nowadays, a big part of people rely on available content in social media in their decisions (e.g., reviews and feedback on a topic or product). The possibility that anybody can leave a review provides a golden opportunity for spammers to write spam reviews about products and services for different interests. Identifying these spammers and the spam content is a hot topic of research, and although a considerable number of studies have been done recently toward this end, but so far the methodologies put forth still barely detect spam reviews, and none of them show the importance of each extracted feature type. In this paper, we propose a novel framework, named NetSpam, which utilizes spam features for modeling review data sets as heterogeneous information networks to map spam detection procedure into a classification problem in such networks. Using the importance of spam features helps us to obtain better results in terms of different metrics experimented on real-world review data sets from Yelp and Amazon Web sites. The results show that NetSpam outperforms the existing methods and among four categories of features, including review-behavioral, user-behavioral, review-linguistic, and user-linguistic, the first type of features performs better than the other categories.
Modeling bug report quality

Software developers spend a significant portion of their resources handling user-submitted bug reports. For software that is widely deployed, the number of bug reports typically outstrips the resources available to triage them. As a result, some reports may be dealt with too slowly or not at all. 
 We present a descriptive model of bug report quality based on a statistical analysis of surface features of over 27,000 publicly available bug reports for the Mozilla Firefox project. The model predicts whether a bug report is triaged within a given amount of time. Our analysis of this model has implications for bug reporting systems and suggests features that should be emphasized when composing bug reports. 
 We evaluate our model empirically based on its hypothetical performance as an automatic filter of incoming bug reports. Our results show that our model performs significantly better than chance in terms of precision and recall. In addition, we show that our modelcan reduce the overall cost of software maintenance in a setting where the average cost of addressing a bug report is more than 2% of the cost of ignoring an important bug report.
Semantic interoperability in the Internet of Things: An overview from the INTER-IoT perspective

The Internet of Things (IoT) idea, explored across the globe, brings about an important issue: how to achieve interoperability among multiple existing (and constantly created) IoT platforms. In this context, in January 2016, the European Commission has funded seven projects that are to deal with various aspects of interoperability in the Internet of Things. Among them, the INTERIoT project is aiming at the design and implementation of, and experimentation with, an open cross-layer framework and associated methodology to provide voluntary interoperability among heterogeneous IoT platforms. While the project considers interoperability across all layers of the software stack, we are particularly interested in answering the question: how ontologies and semantic data processing can be harnessed to facilitate interoperability across the IoT landscape. Henceforth, we have engaged in a fact nding mission to establish what is currently at our disposal when semantic interoperability is concerned. Since the INTER-IoT project is initially driven by two use cases originating from (i) (e/m)Health and (ii) transportation and logistics, these two application domains were used to provide context for our search. The paper summarizes our ndings and provides foundation for developing methods and tools for supporting semantic interoperability in the INTER-IoT project (and beyond).
A review of analgesic and emotive breathing: a multidisciplinary approach

The diaphragm is the primary muscle involved in breathing and other non-primarily respiratory functions such as the maintenance of correct posture and lumbar and sacroiliac movement. It intervenes to facilitate cleaning of the upper airways through coughing, facilitates the evacuation of the intestines, and promotes the redistribution of the body's blood. The diaphragm also has the ability to affect the perception of pain and the emotional state of the patient, functions that are the subject of this article. The aim of this article is to gather for the first time, within a single text, information on the nonrespiratory functions of the diaphragm muscle and its analgesic and emotional response functions. It also aims to highlight and reflect on the fact that when the diaphragm is treated manually, a daily occurrence for manual operators, it is not just an area of musculature that is treated but the entire body, including the psyche. This reflection allows for a multidisciplinary approach to the diaphragm and the collaboration of various medical and nonmedical practitioners, with the ultimate goal of regaining or improving the patient's physical and mental well-being.
Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics

De-noising and extraction of the weak signature are crucial to fault prognostics in which case features are often very weak and masked by noise. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, the performance of wavelet decomposition-based de-noising and wavelet filter-based de-noising methods are compared based on signals from mechanical defects. The comparison result reveals that wavelet filter is more suitable and reliable to detect a weak signature of mechanical impulse-like defect signals, whereas the wavelet decomposition de-noising method can achieve satisfactory results on smooth signal detection. In order to select optimal parameters for the wavelet filter, a two-step optimization process is proposed. Minimal Shannon entropy is used to optimize the Morlet wavelet shape factor. A periodicity detection method based on singular value decomposition (SVD) is used to choose the appropriate scale for the wavelet transform. The signal de-noising results from both simulated signals and experimental data are presented and both support the proposed method.   haiqiu@uwm.edu (H. Qiu), jaylee@uwm.edu (J. Lee), jinglin@mail.ioc.ac.cn (J. Lin).
Design and development of low-cost EOG acquisition circuit for HMI application

In this paper we have proposed a low-cost electrooculogram (EOG) acquisition system that can be used efficiently in Human Machine Interface (HMI) systems. The proposed system consists of an Op-Amp based EEG/EOG amplifier circuit and ATMega8 microcontroller for analog to digital conversion and transferring of acquired data to PC. In this system five electrodes are used to acquire eye blinking, horizontal and vertical eye movements. Electrooculogram signal provides an alternate channel of communication between a human brain and a machine or robot for the people with disabilities which cannot move their hand and/or leg but they can think or they can move their eyes. In this system, the signals are first captured using EEG surface electrodes, amplified, filtered and then converted into digital form before stored into PC.
Hierarchical Reinforcement Learning: A Survey

Reinforcement Learning (RL) has been an interesting research area in Machine Learning and AI. Hierarchical Reinforcement Learning (HRL) that decomposes the RL problem into sub-problems where solving each of which will be more powerful than solving the entire problem will be our concern in this paper. A review of the state-of-the-art of HRL has been investigated. Different HRL-based domains have been highlighted. Different problems in such different domains along with some proposed solutions have been addressed. It has been observed that HRL has not yet been surveyed in the current existing research; the reason that motivated us to work on this paper. Concluding remarks are presented. Some ideas have been emerged during the work on this research and have been proposed for pursuing a future research.