nils-herrmann/clean_scirepeval huggingface dataset:
The use of blended learning in the teaching of piano playing and singing in education faculties

One of the core performing courses in institutions for pre-school teacher training is simultaneous singing and piano playing. To ensure sufficient training hours, it is important to improve teaching methods. As a way to improve the teaching of simultaneous signing and piano playing in a large class, we have incorporated blended learning, in which students are required (1) to submit videos of their performance, and (2) to view and study e-learning materials. We have analyzed how each of these requirements improved students !Gperformance skills in singing and piano playing, and found that they substantially reduce the time required for individual lessons.
Festival visitors’ satisfaction and loyalty: An example of small, local, and municipality organized festival

Festivals have been proliferating worldwide, and local authorities are either supporting, or organizing small, local festivals to enhance the attractiveness of the destination for non-local visitors. Festivals are also very effective tools for developing destination image, revitalizing economy, culture, traditions, building civic pride, raising funds for special, civic or charitable projects, and providing opportunities for the community to deal with fi ne arts. Th is situation increases the importance of factors related to the satisfaction and loyalty of festival visitors, especially for small and local festivals. Th erefore, drawing on the existing literature and an assumption that festivalscape is the most important contributor to visitors’ satisfaction and loyalty in the context of a small, local and municipality organized annual festivals, the present study aims to identify factors related to the festivalscape that determine visitors’ satisfaction and loyalty by using a structural equation modeling. Th e study examines several variables as the antecedents of the festival visitors’ satisfaction and loyalty such as staff , festival area, food, souvenir, informational adequacy and convenience. As a result of the analysis, the study reveals three dimensions related to the festivalscape environmental factors which are food, festival area, and convenience and examines how these factors aff ect the visitors’ satisfaction and, in turn, their loyalty.
Delay alignment of top-and sub loudspeaker systems ; a survey of new and old methods

A couple of popular methods are presented with their benefits and drawbacks. Commonly used methods are using wrapped phase and impulse response. With real time FFT analysis, magnitude and time domain can be analyzed simultaneously. Filtered impulse response and Cepstrum analysis are helpful tools when the spectral content differs and make it hard to analyse the impulse response. To make a successful time alignment the measurements must be anechoic. Methods such as multiple time windowing and averaging in frequency domain are presented. Group-delay and wavelets analysis are used to evaluate the measurements.
Estimating the Benefits and Risks of Implementing E-Procurement

In recent years, organizations have invested heavily in e-procurement technology solutions. However, an estimation of the value of the technology-enabled procurement process is often lacking. Our paper presents a rigorous methodological approach to the analysis of e-procurement benefits. Business process simulations are used to analyze the benefits of both technological and organizational changes related to e-procurement. The approach enables an estimation of both the average and variability of procurement costs and benefits, workload, and lead times. In addition, the approach enables optimization of a procurement strategy (e.g., approval levels). Finally, an innovative approach to estimation of value at risk is shown.
Multidimensional assessment of coping: a critical evaluation.

Several existing self-report measures of coping and the relevant research using these instruments are reviewed. Many of these coping measures suffer from a variety of psychometric weaknesses. A self-report instrument, the Multidimensional Coping Inventory (MCI), was constructed that identifies 3 types of coping styles: task-oriented, emotion-oriented, and avoidance-oriented coping. Support for the multidimensional nature of the MCI is presented, along with support for the reliability of the MCI coping scales. Two studies are presented that assessed the validity of the MCI. The 1st study assessed the construct validity of the MCI by comparing it with the Ways of Coping Questionnaire. The 2nd study also assessed the criterion validity of the MCI by comparing it with measures of depression, anxiety, Type A behaviour, neuroticism, and extraversion. Overall, the results suggest that the MCI is a valid and highly reliable multidimensional measure of coping styles.
Probabilistic Models for Text Mining

A number of probabilistic methods such as LDA, hidden Markov models, Markov random fields have arisen in recent years for probabilistic analysis of text data. This chapter provides an overview of a variety of probabilistic models for text mining. The chapter focuses more on the fundamental probabilistic techniques, and also covers their various applications to different text mining problems. Some examples of such applications include topic modeling, language modeling, document classification, document clustering, and information extraction.
Neural Decomposition of Time-Series Data for Effective Generalization

We present a neural network technique for the analysis and extrapolation of time-series data called neural decomposition (ND). Units with a sinusoidal activation function are used to perform a Fourier-like decomposition of training samples into a sum of sinusoids, augmented by units with nonperiodic activation functions to capture linear trends and other nonperiodic components. We show how careful weight initialization can be combined with regularization to form a simple model that generalizes well. Our method generalizes effectively on the Mackey–Glass series, a data set of unemployment rates as reported by the U.S. Department of Labor Statistics, a time-series of monthly international airline passengers, the monthly ozone concentration in downtown Los Angeles, and an unevenly sampled time series of oxygen isotope measurements from a cave in north India. We find that ND outperforms popular time-series forecasting techniques, including long short-term memory network, echo-state networks, autoregressive integrated moving average (ARIMA), seasonal ARIMA, support vector regression with a radial basis function, and Gashler and Ashmore’s model.
Scalable agent alignment via reward modeling: a research direction

One obstacle to applying reinforcement learning algorithms to real-world problems is the lack of suitable reward functions. Designing such reward functions is difficult in part because the user only has an implicit understanding of the task objective. This gives rise to the agent alignment problem: how do we create agents that behave in accordance with the user’s intentions? We outline a high-level research direction to solve the agent alignment problem centered around reward modeling: learning a reward function from interaction with the user and optimizing the learned reward function with reinforcement learning. We discuss the key challenges we expect to face when scaling reward modeling to complex and general domains, concrete approaches to mitigate these challenges, and ways to establish trust in the resulting agents.
Active noise cancellation with TMS320C5402 DSP starter kit

The paper describes the use TMS320C5402 DSP for single channel active noise cancellation (ANC) in duct system. The canceller uses a feedback control topology and is designed to cancel narrowband periodic tones. The signal is processed with well-known filtered-X least mean square (filtered-X LMS) Algorithm in the digital signal processing. The paper describes the hardware and use chip support libraries for data streaming. The FXLMS algorithm is written in assembly language callable from C main program. The results obtained are compatible to the expected result in the literature available. The paper highlights the features of cancellation and analyzes its performance at different gain and frequency.
Decision Guidance Models for Microservice Monitoring

Decision guidance models are a means for design space exploration and documentation. In this paper, we present decision guidance models for microservice monitoring. The selection of a monitoring system is an essential part of each microservice architecture due to the high level of dynamic structure and behavior of such a system. We present decision guidance models for generation of monitoring data, data management, processing monitoring data, and for disseminating and presenting monitoring information to stakeholders. The presented models have been derived from literature, our previous work on monitoring for distributed systems and microservice-based systems, and by analyzing existing monitoring systems. The developed models have been used for discussing monitoring requirements for a microservice-based system with a company in the process automation domain. They are part of a larger effort for developing decision guidance models for microservice architecture in general.
Trends in distributed artificial intelligence

Distributed artificial intelligence (DAI) is a subfield of artificial intelligence that deals with interactions of intelligent agents. Precisely, DAI attempts to construct intelligent agents that make decisions that allow them to achieve their goals in a world populated by other intelligent agents with their own goals. This paper discusses major concepts used in DAI today. To do this, a taxonomy of DAI is presented, based on the social abilities of an individual agent, the organization of agents, and the dynamics of this organization through time. Social abilities are characterized by the reasoning about other agents and the assessment of a distributed situation. Organization depends on the degree of cooperation and on the paradigm of communication. Finally, the dynamics of organization is characterized by the global coherence of the group and the coordination between agents. A reasonably representative review of recent work done in DAI field is also supplied in order to provide a better appreciation of this vibrant AI field. The paper concludes with important issues in which further research in DAI is needed.
Finding Perceptually Closed Paths in Sketches and Drawings

Closed or nearly closed regions are an important form of perceptual structure arising both in natural imagery and in many forms of human-created imagery including sketches, line art, graphics, and formal drawings. This paper presents an effective algorithm especially suited for finding perceptually salient, compact closed region structure in hand-drawn sketches and line art. We start with a graph of curvilinear fragments whose proximal endpoints form junctions. The key problem is to manage the search of possible path continuations through junctions in an effort to find paths satisfying global criteria for closure and figural salience. We identify constraints particular to this domain for ranking path continuations through junctions, based on observations of the ways that junctions arise in line drawings. In particular, we delineate the roles of the principle of good continuation versus maximally turning paths. Best-first bidirectional search checks for the cleanest, most obvious paths first, then reverts to more exhaustive search to find paths cluttered by blind alleys. Results are demonstrated on line drawings from several sources including line art, engineering drawings, sketches on whiteboards, as well as contours from photographic imagery.
Information Visualization : Challenge for the Humanities

Digital archiving creates a vast store of knowledge that can be accessed only through digital tools. Users of this information will need fluency in the tools of digital access, exploration, visualization, analysis, and collaboration. This paper proposes that this fluency represents a new form of literacy, which must become fundamental for humanities scholars. Tools influence both the creation and the analysis of information. Whether using pen and paper, Microsoft Office, or Web 2.0, scholars base their process, production, and questions on the capabilities their tools offer them. Digital archiving and the interconnectivity of the Web provide new challenges in terms of quantity and quality of information. They create a new medium for presentation as well as a foundation for collaboration that is independent of physical location. Challenges for digital humanities include: • developing new genres for complex information presentation that can be shared, analyzed, and compared; • creating a literacy in information analysis and visualization that has the same rigor and richness as current scholarship; and • expanding classically text-based pedagogy to include simulation, animation, and spatial and geographic representation.
LineNet: a Zoomable CNN for Crowdsourced High Definition Maps Modeling in Urban Environments

High Definition (HD) maps play an important role in modern traffic scenes. However, the development of HD maps coverage grows slowly because of the cost limitation. To efficiently model HD maps, we proposed a convolutional neural network with a novel prediction layer and a zoom module, called LineNet. It is designed for state-of-the-art lane detection in an unordered crowdsourced image dataset. And we introduced TTLane, a dataset for efficient lane detection in urban road modeling applications. Combining LineNet and TTLane, we proposed a pipeline to model HD maps with crowdsourced data for the first time. And the maps can be constructed precisely even with inaccurate crowdsourced data.
On the Convergence Rate of Training Recurrent Neural Networks

Despite the huge success of deep learning, our understanding to how the non-convex neural networks are trained remains rather limited. Most of existing theoretical works only tackle neural networks with one hidden layer, and little is known for multi-layer neural networks. Recurrent neural networks (RNNs) are special multi-layer networks extensively used in natural language processing applications. They are particularly hard to analyze, comparing to feedforward networks, because the weight parameters are reused across the entire time horizon. We provide arguably the first theoretical understanding to the convergence speed of training RNNs (when activation functions are present). Specifically, when the weights are randomly initialized, and when the number of neurons is sufficiently large—meaning polynomial in the training data size and the time horizon— we show that gradient descent and stochastic gradient descent both minimize the regression loss in a linear convergence rate, that is, ε ∝ e . ∗Version 1 appears on this date. Version 2 adds some missing citations of prior work. Work was done when Yuanzhi Li and Zhao Song were 2018 summer interns at Microsoft Research, Redmond. We would like to specially thank Greg Yang, Sébastien Bubeck and Ofer Dekel for useful conversations.
A Survey on Methods of Plant Disease Detection

Plant disease detection is emerging field in India as agriculture is important sector in Economy and Social life. Earlier unscientific methods were in existence. Gradually with technical and scientific advancement, more reliable methods through lowest turnaround time are developed and proposed for early detection of plant disease. Such techniques are widely used and proved beneficial to farmers as detection of plant disease is possible with minimal time span and corrective actions are carried out at appropriate time. In this paper, we studied and evaluated existing techniques for detection of plant diseases to get clear outlook about the techniques and methodologies followed. The detection of plant disease is significantly based on type of family plants and same is carried out in two phases as segmentation and classification. Here, we have discussed existing segmentation method along with classifiers for detection of diseases in Monocot and Dicot family plant.
3D printing for soft robotics – a review

Soft robots have received an increasing attention due to their advantages of high flexibility and safety for human operators but the fabrication is a challenge. Recently, 3D printing has been used as a key technology to fabricate soft robots because of high quality and printing multiple materials at the same time. Functional soft materials are particularly well suited for soft robotics due to a wide range of stimulants and sensitive demonstration of large deformations, high motion complexities and varied multi-functionalities. This review comprises a detailed survey of 3D printing in soft robotics. The development of key 3D printing technologies and new materials along with composites for soft robotic applications is investigated. A brief summary of 3D-printed soft devices suitable for medical to industrial applications is also included. The growing research on both 3D printing and soft robotics needs a summary of the major reported studies and the authors believe that this review article serves the purpose.
PHP and SQL made simple

The book Build Your Own Database Driven Website Using PHP & MySQL by Kevin Yank provides a hands-on look at what's involved in building a database-driven Web site. The author does a good job of patiently teaching the reader how to install and configure PHP 5 and MySQL to organize dynamic Web pages and put together a viable content management system. At just over 350 pages, the book is rather small compared to a lot of others on the topic, but it contains all the essentials. The author employs excellent teaching techniques to set up the foundation stone by stone and then grouts everything solidly together later in the book. This book aims at intermediate and advanced Web designers looking to make the leap to server-side programming. The author assumes his readers are comfortable with simple HTML. He provides an excellent introduction to PHP and MySQL (including installation) and explains how to make them work together. The amount of material he covers guarantees that almost any reader will benefit.
Electricity generation using membrane and salt bridge microbial fuel cells.

Microbial fuel cells (MFCs) can be used to directly generate electricity from the oxidation of dissolved organic matter, but optimization of MFCs will require that we know more about the factors that can increase power output such as the type of proton exchange system which can affect the system internal resistance. Power output in a MFC containing a proton exchange membrane was compared using a pure culture (Geobacter metallireducens) or a mixed culture (wastewater inoculum). Power output with either inoculum was essentially the same, with 40+/-1mW/m2 for G. metallireducens and 38+/-1mW/m2 for the wastewater inoculum. We also examined power output in a MFC with a salt bridge instead of a membrane system. Power output by the salt bridge MFC (inoculated with G. metallireducens) was 2.2mW/m2. The low power output was directly attributed to the higher internal resistance of the salt bridge system (19920+/-50 Ohms) compared to that of the membrane system (1286+/-1Ohms) based on measurements using impedance spectroscopy. In both systems, it was observed that oxygen diffusion from the cathode chamber into the anode chamber was a factor in power generation. Nitrogen gas sparging, L-cysteine (a chemical oxygen scavenger), or suspended cells (biological oxygen scavenger) were used to limit the effects of gas diffusion into the anode chamber. Nitrogen gas sparging, for example, increased overall Coulombic efficiency (47% or 55%) compared to that obtained without gas sparging (19%). These results show that increasing power densities in MFCs will require reducing the internal resistance of the system, and that methods are needed to control the dissolved oxygen flux into the anode chamber in order to increase overall Coulombic efficiency.
Democratizing Production-Scale Distributed Deep Learning

The interest and demand for training deep neural networks have been experiencing rapid growth, spanning a wide range of applications in both academia and industry. However, training them distributed and at scale remains difficult due to the complex ecosystem of tools and hardware involved. One consequence is that the responsibility of orchestrating these complex components is often left to one-off scripts and glue code customized for specific problems. To address these restrictions, we introduce Alchemist an internal service built at Apple from the ground up for easy, fast, and scalable distributed training. We discuss its design, implementation, and examples of running different flavors of distributed training. We also present case studies of its internal adoption in the development of autonomous systems, where training times have been reduced by 10x to keep up with the ever-growing data collection.
Start Today or the Very Last Day ? The Relationships Among Self-Compassion , Motivation , and Procrastination

College students differ in their approaches to challenging course assignments. While some prefer to begin their assignments early, others postpone their work until the last minute. The present study adds to the procrastination literature by examining the links among self-compassionate attitudes, motivation, and procrastination tendency. A sample of college undergraduates completed four online surveys. Individuals with low, moderate, and high levels of self-compassion were compared on measures of motivation anxiety, achievement goal orientation, and procrastination tendency. Data analyses revealed that individuals with high self-compassion reported dramatically less motivation anxiety and procrastination tendency than those with low or moderate self-compassion. The practical importance of studying self-views as potential triggers for procrastination behavior and directions for future research are discussed.
A fraud management system architecture for next-generation networks.

This paper proposes an original architecture for a fraud management system (FMS) for convergent. Next-generation networks (NGNs), which are based on the Internet protocol (IP). The architecture has the potential to satisfy the requirements of flexibility and application-independency for effective fraud detection in NGNs that cannot be met by traditional FMSs. The proposed architecture has a thorough four-stage detection process that analyses billing records in IP detail record (IPDR) format - an emerging IP-based billing standard - for signs of fraud. Its key feature is its usage of neural networks in the form of self-organising maps (SOMs) to help uncover unknown NGN fraud scenarios. A prototype was implemented to test the effectiveness of using a SOM for fraud detection and is also described in the paper.
A Survey of Word Embeddings Evaluation Methods

Word embeddings are real-valued word representations able to capture lexical semantics and trained on natural language corpora. Models proposing these representations have gained popularity in the recent years, but the issue of the most adequate evaluation method still remains open. This paper presents an extensive overview of the field of word embeddings evaluation, highlighting main problems and proposing a typology of approaches to evaluation, summarizing 16 intrinsic methods and 12 extrinsic methods. I describe both widely-used and experimental methods, systematize information about evaluation datasets and discuss some key challenges.