nils-herrmann/clean_scirepeval huggingface dataset:
Python for Microwave and RF Engineers [Application Notes]

Python is a powerful programming language for handling engineering and scientific computational tasks efficiently [1]-[5]. Used by companies such as Google and Intel and by organizations including NASA and Los Alamos National Laboratory, it offers an extremely wide selection of tools for tasks such as scientific computing, signal processing, Web site construction, database programming, and graphical user interface (GUI) design. The language is platform independent with most programs running on Linux, Microsoft Windows, or MAC OS virtually unchanged. Python has been distributed as a part of Open Source Initiative, and most versions are General Public License (GPL) compatible. In microwave and radio frequency (RF) engineering, it can be used for numerical programming, automated RF testing, automated monolithic microwave integrated-circuit (MMIC) layout generation, automated netlist generation and simulator sequencing, and other tasks. In this article, we are going to provide examples demonstrating the application areas of Python in microwave and RF engineering. We are also going to give a brief introduction to the language highlighting its salient features.
Flame detection in video using hidden Markov models

This paper proposes a novel method to detect flames in video by processing the data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame flicker process is also detected by using a hidden Markov model. Markov models representing the flame and flame colored ordinary moving objects are used to distinguish flame flicker process from motion of flame colored moving objects. Spatial color variations in flame are also evaluated by the same Markov models, as well. These clues are combined to reach a final decision. False alarms due to ordinary motion of flame colored moving objects are greatly reduced when compared to the existing video based fire detection systems.
Intentions to use social media in organizing and taking vacation trips

0747-5632/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.chb.2010.05.022 * Corresponding author.  E-mail addresses: eparra@ull.es (E. Parra-López), Bulchand-Gidumal), dgtano@ull.es (D. Gutiérrez-Ta Armas). This work proposes a theoretical model to explain the factors determining the intentions to use social media when organizing and taking vacation trips. Understanding the antecedents of the tourists’ use of these technologies is considered to be crucial for organization managers and destination policy makers. This use of social media technologies determines which elements of the trip might be used by the tourist thus having a great impact on the market. The model and its hypotheses have been tested by means of an approach based on structural equations with the PLS technique. The study was conducted on a sample of 404 individuals who normally use the Internet and had traveled on vacation in the previous 12 months. The conclusions of the study reveal that the intentions to use social media are directly influenced by the perceived benefits of that use (functional, psychological and hedonic and social); however, the costs do not significantly affect the predisposition to use such technologies. It is also shown that there is a series of incentives such as altruism, availability, individual predisposition or trust in the contributions of others which facilitate and promote the use of this type of technology when organizing and taking tourist trips.
A self biased operational amplifier at ultra low power supply voltage

This paper discusses the design of a self-biased folded cascode operational amplifier at an ultra low power supply voltage. The proposed design is first of its kind at 0.5 V where self-biasing techniques are used to reduce power and area overheads. The self-biasing scheme in this design is developed by using a current mirror for low voltage operation. This design is implemented in a 90 nm CMOS technology using Cadence General Purpose Design Kit (GPDK).
Structured Transforms for Small-Footprint Deep Learning

We consider the task of building compact deep learning pipelines suitable for deployment on storage and power constrained mobile devices. We propose a unified framework to learn a broad family of structured parameter matrices that are characterized by the notion of low displacement rank. Our structured transforms admit fast function and gradient evaluation, and span a rich range of parameter sharing configurations whose statistical modeling capacity can be explicitly tuned along a continuum from structured to unstructured. Experimental results show that these transforms can significantly accelerate inference and forward/backward passes during training, and offer superior accuracy-compactness-speed tradeoffs in comparison to a number of existing techniques. In keyword spotting applications in mobile speech recognition, our methods are much more effective than standard linear low-rank bottleneck layers and nearly retain the performance of state of the art models, while providing more than 3.5-fold compression.
Learning to Generate Product Reviews from Attributes

Automatically generating product reviews is a meaningful, yet not well-studied task in sentiment analysis. Traditional natural language generation methods rely extensively on hand-crafted rules and predefined templates. This paper presents an attention-enhanced attribute-to-sequence model to generate product reviews for given attribute information, such as user, product, and rating. The attribute encoder learns to represent input attributes as vectors. Then, the sequence decoder generates reviews by conditioning its output on these vectors. We also introduce an attention mechanism to jointly generate reviews and align words with input attributes. The proposed model is trained end-to-end to maximize the likelihood of target product reviews given the attributes. We build a publicly available dataset for the review generation task by leveraging the Amazon book reviews and their metadata. Experiments on the dataset show that our approach outperforms baseline methods and the attention mechanism significantly improves the performance of our model.
Practical Relay Attack on Contactless Transactions by Using NFC Mobile Phones

Contactless technology is widely used in security sensitive applications, including identification, payment and access-control systems. Near Field Communication (NFC) is a short-range contactless technology allowing mobile devices to act primarily as either a reader or a token. Relay attacks exploit the assumption that a contactless token within communication range is in close proximity, by placing a proxy-token in range of a contactless reader and relaying communication over a greater distance to a proxy-reader communicating with the authentic token. It has been theorised that NFC-enabled mobile phones could be used as a generic relay attack platform without any additional hardware, but this has not been successfully demonstrated in practice. We present a practical implementation of an NFC-enabled relay attack, requiring only suitable mobile software applications. This implementation reduces the complexity of relay attacks and therefore has potential security implications for current contactless systems. We also discuss countermeasures to mitigate the attack.
Physical Demands of Different Positions in FA Premier League Soccer.

The purpose of this study was to evaluate the physical demands of English Football Association (FA) Premier League soccer of three different positional classifications (defender, midfielder and striker). Computerised time-motion video-analysis using the Bloomfield Movement Classification was undertaken on the purposeful movement (PM) performed by 55 players. Recognition of PM had a good inter-tester reliability strength of agreement (κ= 0.7277). Players spent 40.6 ± 10.0% of the match performing PM. Position had a significant influence on %PM time spent sprinting, running, shuffling, skipping and standing still (p < 0.05). However, position had no significant influence on the %PM time spent performing movement at low, medium, high or very high intensities (p > 0.05). Players spent 48.7 ± 9.2% of PM time moving in a directly forward direction, 20.6 ± 6.8% not moving in any direction and the remainder of PM time moving backward, lateral, diagonal and arced directions. The players performed the equivalent of 726 ± 203 turns during the match; 609 ± 193 of these being of 0° to 90° to the left or right. Players were involved in the equivalent of 111 ± 77 on the ball movement activities per match with no significant differences between the positions for total involvement in on the ball activity (p > 0.05). This study has provided an indication of the different physical demands of different playing positions in FA Premier League match-play through assessment of movements performed by players.  spent ~40% of the match performing Pur-poseful Movement (PM).Position had a significant influence on %PM time spent performing each motion class except walking and jogging. Players performed >700 turns in PM, most of these being of 0°-90°.Strikers performed most high to very high intensity activity and most contact situations.Defenders also spent a significantly greater %PM time moving backwards than the other two posi-tions.Different positions could benefit from more specific conditioning programs.
K-modestream algorithm for clustering categorical data streams

Clustering categorical data streams is a challenging problem because new data points are continuously adding to the already existing database at rapid pace and there exists no natural order among the categorical values. Recently, some algorithms have been discussed to tackle the problem of clustering the categorical data streams. However, in all these schemes the user needs to pre-specify the number of clusters, which is not trivial, and it renders to inefficient in the data stream environment. In this paper, we propose a new clustering algorithm, named it as k-modestream, which follows the k-modes algorithm paradigm to dynamically cluster the categorical data streams. It automatically computes the number of clusters and their initial modes simultaneously at regular time intervals. We analyse the time complexity of our scheme and perform various experiments using the synthetic and real world datasets to evaluate its efficacy.
Modeling crowdsourcing systems: design and analysis of incentive mechanism and rating system

Over the past few years, we have seen an increasing popularity of crowdsourcing services . Many companies are now providing such services, e.g., Amazon Mechanical Turk , Google Helpouts , and Yahoo! Answers , etc. Briefly speaking, crowdsourcing is an online, distributed problem solving paradigm and business production platform. It uses the power of today’s Internet to solicit the collective intelligence of large number of users. Relying on the wisdom of the crowd to solve posted tasks (or problems), crowdsourcing has become a promising paradigm to obtain “solutions” which can have higher quality or lower costs than the conventional method of solving problems via specialized employees or contractors in a company. Typically, a crowdsourcing system operates with three basic components: Users, tasks and rewards. Users are classified into requesters and workers. A user can be a requester or a worker, and in some cases, a user can be a requester/worker at the same time. Requesters outsource tasks to workers and associate each task with certain rewards, which will be granted to the workers who solve the task. Workers, on the other hand, solve the assigned tasks and reply to requesters with solutions, and then take the reward, which can be in form of money , entertainment [7] or altruism [8], etc. To have a successful crowdsourcing website, it is pertinent to attract high volume of participation of users (requesters and workers), and at the same time, solutions by workers have to be of high quality. In this paper we design a rating system and a mechanism to encourage users to participate, and incentivize workers to high quality solutions. First, we develop a game-theoretic model to characterize workers’ strategic behavior. We then design a class effective incentive mechanisms which consist of a task bundling scheme and a rating system, and pay workers according to solution ratings from requesters. We develop a model to characterize the design space of a class of commonly users rating systems– threshold based rating system. We quantify the impact of such rating systems, and the bundling scheme on reducing requesters’ reward payment in guaranteeing high quality solutions. We find out that a simplest rating system, e.g., two rating points, is an effective system in which requesters only need to provide binary feedbacks to indicate whether they are satisfied or not with a solution.
Animating pictures with stochastic motion textures

In this paper, we explore the problem of enhancing still pictures with subtly animated motions. We limit our domain to scenes containing passive elements that respond to natural forces in some fashion. We use a semi-automatic approach, in which a human user segments the scene into a series of layers to be individually animated. Then, a "stochastic motion texture" is automatically synthesized using a spectral method, i.e., the inverse Fourier transform of a filtered noise spectrum. The motion texture is a time-varying 2D displacement map, which is applied to each layer. The resulting warped layers are then recomposited to form the animated frames. The result is a looping video texture created from a single still image, which has the advantages of being more controllable and of generally higher image quality and resolution than a video texture created from a video source. We demonstrate the technique on a variety of photographs and paintings.
Outcomes in hand rehabilitation using occupational therapy services.

The purpose of this study was to measure functional outcomes after outpatient occupational therapy for clients who had upper-extremity injury and surgery or both.



A sample of 33 clients referred to occupational therapy outpatient intervention was recruited from five clinics in Ohio. The Canadian Occupational Performance Measure (COPM) was used to guide the occupational therapy sessions and to measure outcomes. The Disability of Arm, Shoulder, and Hand (DASH) questionnaire and the Short Form 36 (SF-36) were also administered to the clients pre and post 6 to 8 weeks of hand rehabilitation services. Two to three months after discharge, the clients responded by the telephone to the Community Integration Questionnaire.



The clients received a mean of 13 hours of outpatient occupational therapy services and received no other rehabilitation service. Functional performance gains following 6 to 8 weeks of services were significant (COPM; t(32) = -11.5, p < .001; t(32) = -11.31, p < .001; DASH; t(32) = 9.22, p < .001; t(32) = -9.02, p < .001). Effect sizes on the COPM and DASH ranged between 1.43 and 2.52. Progress in the clients' goals was moderately correlated to progress in functional measures.



Clients with upper-extremity injury or surgery made strong, positive gains in functional measures following client-centered occupational therapy services. The COPM was the most sensitive to client change, followed by the DASH, and then the SF-36.
Finding Influential Training Samples for Gradient Boosted Decision Trees

We address the problem of finding influential training samples for a particular case of tree ensemble-based models, e.g., Random Forest (RF) or Gradient Boosted Decision Trees (GBDT). A natural way of formalizing this problem is studying how the model’s predictions change upon leave-one-out retraining, leaving out each individual training sample. Recent work has shown that, for parametric models, this analysis can be conducted in a computationally efficient way. We propose several ways of extending this framework to non-parametric GBDT ensembles under the assumption that tree structures remain fixed. Furthermore, we introduce a general scheme of obtaining further approximations to our method that balance the trade-off between performance and computational complexity. We evaluate our approaches on various experimental setups and use-case scenarios and demonstrate both the quality of our approach to finding influential training samples in comparison to the baselines and its computational
Author ' s personal copy Grammatical aspect and mental simulation

When processing sentences about perceptible scenes and performable actions, language understanders activate perceptual and motor systems to perform mental simulations of those events. But little is known about exactly what linguistic elements activate modality-specific systems during language processing. While it is known that content words, like nouns and verbs, influence the content of a mental simulation, the role of grammar is less well understood. We investigate the role of grammatical markers in mental simulation through two experiments in which we manipulate the meanings of sentences by modifying the grammatical aspect they use. Using the Action-sentence Compatibility Effect (ACE) methodology [Glenberg, A., Kaschak, M. (2002). Grounding language in action. Psychonomic Bulletin and Review, 9, 558–565], we show that progressive sentences about hand motion facilitate manual action in the same direction, while perfect sentences that are identical in every way except their aspect do not. The broader implication of this finding for language processing is that while content words tell understanders what to mentally simulate and what brain regions to use in performing these simulations, grammatical constructions such as aspect modulate how those simulations are performed.
A Sharp PageRank Algorithm with Applications to Edge Ranking and Graph Sparsification

We give an improved algorithm for computing personalized PageRank vectors with tight error bounds which can be as small as Ω(n−p) for any fixed positive integer p. The improved PageRank algorithm is crucial for computing a quantitative ranking of edges in a given graph. We will use the edge ranking to examine two interrelated problems – graph sparsification and graph partitioning. We can combine the graph sparsification and the partitioning algorithms using PageRank vectors to derive an improved partitioning algorithm.
Design and Control for LCL-Based Inverters with Both Grid-Tie and Standalone Parallel Operations

The inductor-capacitor-inductor (LCL) filter allows higher noise attenuation and universal output in which a power conditioning system or an inverter can operate in both grid-tie and standalone modes. In this paper, the LCL filter design considerations including sensor position selection and component selections are discussed for single-phase paralleled inverters operating in both grid-tie and standalone modes. For grid-tie mode operation, each inverter is operating under a single current loop with proportional-resonant controller and admittance path compensation to reduce the steady-state error by providing a high gain at the fundamental frequency. For standalone mode operation, one of the inverters is implemented with a dual-loop controller to regulate the output voltage while the rest inverters operate in single current-loop controller with communication channels in between to ensure the uniformity of current sharing. Both the simulation and experimental results verify that the designed controllers are capable of paralleling inverter operation in grid-tie and standalone modes by adapting to different controller settings while keeping the same hardware setup.
A Framework for Discovering Internal Financial Fraud Using Analytics

In today's knowledge based society, financial fraud has become a common phenomenon. Moreover, the growth in knowledge discovery in databases and fraud audit has made the detection of internal financial fraud a major area of research. On the other hand, auditors find it difficult to apply a majority of techniques in the fraud auditing process and to integrate their domain knowledge in this process. In this Paper a framework called "Knowledge-driven Internal Fraud Detection (KDIFD)" is proposed for detecting internal financial frauds. The framework suggests a process-based approach that considers both forensic auditor's tacit knowledge base and computer-based data analysis and mining techniques. The proposed framework can help auditor in discovering internal financial fraud more efficiently.
HL7 FHIR: An Agile and RESTful approach to healthcare information exchange

This research examines the potential for new Health Level 7 (HL7) standard Fast Healthcare Interoperability Resources (FHIR, pronounced “fire”) standard to help achieve healthcare systems interoperability. HL7 messaging standards are widely implemented by the healthcare industry and have been deployed internationally for decades. HL7 Version 2 (“v2”) health information exchange standards are a popular choice of local hospital communities for the exchange of healthcare information, including electronic medical record information. In development for 15 years, HL7 Version 3 (“v3”) was designed to be the successor to Version 2, addressing Version 2's shortcomings. HL7 v3 has been heavily criticized by the industry for being internally inconsistent even in it's own documentation, too complex and expensive to implement in real world systems and has been accused of contributing towards many failed and stalled systems implementations. HL7 is now experimenting with a new approach to the development of standards with FHIR. This research provides a chronicle of the evolution of the HL7 messaging standards, an introduction to HL7 FHIR and a comparative analysis between HL7 FHIR and previous HL7 messaging standards.
A novel compact ka-band high-rejection diplexer based on substrate integrated waveguide

In this paper, a compact diplexer using substrate integrated waveguide (SIW) resonators is introduced. The diplexer consists of two filters and a circulator. The diplexer channels are 4% and 5% relative bandwidth at 32GHz and 33GHz, respectively. The filters are based on SIW resonators, which have higher Q values. This paper also analyzed the factors which affect the Q value of resonators, and describes the designing procedure of Generalized Chebyshev filters, which used in this paper. The circulator also is on account of SIW. It uses inductive windows to increase bandwidth. At last, the paper gives the diplexer's simulation and measure results. And two results are similar with each other.
A rare case of mesh infection 3 years after a laparoscopic totally extraperitoneal (TEP) inguinal hernia repair.

Late complications after a laparoscopic inguinal hernia repair are extremely rare and have only recently entered into the literature. One such late complication is mesh infection, of which there have been a handful of cases reported in the literature. Mesh infections occurring many years after inguinal hernia repairs are not only of significance because they are not well documented in the literature, and the pathogenesis and risk factors contributing to their development are not well understood. This report details a rare case of mesh infection 3 years after a laparoscopic totally extraperitoneal inguinal hernia repair, describes our management of the condition, highlights the current options for management, and attempts to define its pathophysiology.