nils-herrmann/clean_scirepeval huggingface dataset:
Indecision and avoidant procrastination: the role of morningness-eveningness and time perspective in chronic delay lifestyles.

The authors examined how time orientation and morningness-eveningness relate to 2 forms of procrastination: indecision and avoidant forms. Participants were 509 adults (M age = 49.78 years, SD = 6.14) who completed measures of time orientation, morningness-eveningness, decisional procrastination (i.e., indecision), and avoidant procrastination. Results showed that morningness was negatively related to avoidant procrastination but not decisional procrastination. Overall, the results indicated different temporal profiles for indecision and avoidant procrastinations. Avoidant procrastination related to low future time orientation and low morningness, whereas indecision related to both (a) high negative and high positive past orientations and (b) low present-hedonistic and low future time orientations. The authors inferred that distinct forms of procrastination seem different on the basis of dimensions of time.
A survey of multilinear subspace learning for tensor data

Increasingly large amount of multidimensional data are being generated on a daily basis in many applications. This leads to a strong demand for learning algorithms to extract useful information from these massive data. This paper surveys the field of multilinear subspace learning (MSL) for dimensionality reduction of multidimensional data directly from their tensorial representations. It discusses the central issues of MSL, including establishing the foundations of the field via multilinear projections, formulating a unifying MSL framework for systematic treatment of the problem, examining the algorithmic aspects of typical MSL solutions, and categorizing both unsupervised and supervised MSL algorithms into taxonomies. Lastly, the paper summarizes a wide range of MSL applications and concludes with perspectives on future research directions.
Analysis of a Wideband Circularly Polarized Cylindrical Dielectric Resonator Antenna With Broadside Radiation Coupled With Simple Microstrip Feeding

In this paper, a wideband circularly polarized cylindrical shaped dielectric resonator antenna (DRA) with simple microstrip feed network has been designed and investigated. The proposed design uses dual vertical microstrip lines arranged in a perpendicular fashion to excite fundamental orthogonal hybrid <inline-formula> <tex-math notation="LaTeX">${HE}_{11\delta }^{x}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${HE}_{11\delta }^{y}$ </tex-math></inline-formula> modes in the cylindrical DR. The Phase quadrature relationships between orthogonal modes have been attained by varying corresponding microstrips heights. To ratify the simulation results, an antenna prototype is fabricated and measured. Measured input reflection coefficient and axial ratio bandwidth (at <inline-formula> <tex-math notation="LaTeX">$\Phi =0^{\circ }$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\theta =0^{\circ }$ </tex-math></inline-formula>) of 30.37% (2.82–3.83 GHz) and 24.6% (2.75–3.52 GHz) has been achieved, respectively. This antenna design achieves an average gain of 5.5 dBi and radiation efficiency of above 96% over operational frequency band. Justifiable agreement between simulated and fabricated antenna results are obtained.
FORECASTING STOCK MARKET TRENDS BY LOGISTIC REGRESSION AND NEURAL NETWORKS EVIDENCE FROM KSA STOCK MARKET

Estimating stock market trends is very important for investors to act for future. Kingdom of Saudi Arabia (KSA) stock market is evolving rapidly; so the objective of this paper is to forecast the stock market trends using logistic model and artificial neural network. Logistic model is a variety of probabilistic statistical classification model. It is also used to predict a binary response from a binary predictor. Artificial neural networks are used for forecasting because of their capabilities of pattern recognition and machine learning. Both methods are used to forecast the stock prices of upcoming period. The model has used the preprocessed data set of closing value of TASA Index. The data set encompassed the trading days from 5 April, 2007 to 1 January, 2015. With logistic regression it may be observed that four variables i.e. open price, higher price, lower price and oil can classify up to 81.55% into two categories up and down. While with neural networks The prediction accuracy of the model is high both for the training data (84.12%) and test data (81.84%).
Economic integration in China : Politics and culture

Herrmann-Pillath, Carsten, Libman, Alexander, and Yu, Xiaofan—Economic integration in China: Politics and culture The aim of the paper is to explicitly disentangle the role of political and cultural boundaries as factors of fragmentation of economies within large countries. On the one hand, local protectionism plays a substantial role in many federations and decentralized states. On the other hand, if the country exhibits high level of cultural heterogeneity, it may also contribute to the economic fragmentation; however, this topic has received significantly less attention in the literature. This paper looks at the case of China and proxies the cultural heterogeneity by the heterogeneity of local dialects. It shows that the effect of politics clearly dominates that of culture: while provincial borders seem to have a strong influence disrupting economic ties, economic linkages across provinces, even if the regions fall into the same linguistic zone, are rather weak and, on the contrary, linguistic differences within provinces do not prevent economic integration. For some language zones we do, however, find a stronger effect on economic integration.  (2014) 470–492. Frankfurt School of Finance and Management, Germany; Russian Academy of Sciences, Russia. 2013 Association for Comparative Economic Studies
Exploring generative perspective of convolutional neural networks by learning random field models ∗

This paper studies the convolutional neural network (ConvNet or CNN) from a statistical modeling perspective. The ConvNet has proven to be a very successful discriminative learning machine. In this paper, we explore the generative perspective of the ConvNet. We propose to learn Markov random field models called FRAME (Filters, Random field, And Maximum Entropy) models using the highly sophisticated filters pre-learned by the ConvNet on the big ImageNet dataset. We show that the learned models can generate realistic and rich object and texture patterns in natural scenes. We explain that each learned model corresponds to a new ConvNet unit at the layer above the layer of filters employed by the model. We further show that it is possible to learn a generative ConvNet model with a new layer of multiple filters, and the learning algorithm admits an EM interpretation with binary latent variables.
Précis of statistical significance: rationale, validity, and utility.

The null-hypothesis significance-test procedure (NHSTP) is defended in the context of the theory-corroboration experiment, as well as the following contrasts: (a) substantive hypotheses versus statistical hypotheses, (b) theory corroboration versus statistical hypothesis testing, (c) theoretical inference versus statistical decision, (d) experiments versus nonexperimental studies, and (e) theory corroboration versus treatment assessment. The null hypothesis can be true because it is the hypothesis that errors are randomly distributed in data. Moreover, the null hypothesis is never used as a categorical proposition. Statistical significance means only that chance influences can be excluded as an explanation of data; it does not identify the nonchance factor responsible. The experimental conclusion is drawn with the inductive principle underlying the experimental design. A chain of deductive arguments gives rise to the theoretical conclusion via the experimental conclusion. The anomalous relationship between statistical significance and the effect size often used to criticize NHSTP is more apparent than real. The absolute size of the effect is not an index of evidential support for the substantive hypothesis. Nor is the effect size, by itself, informative as to the practical importance of the research result. Being a conditional probability, statistical power cannot be the a priori probability of statistical significance. The validity of statistical power is debatable because statistical significance is determined with a single sampling distribution of the test statistic based on H0, whereas it takes two distributions to represent statistical power or effect size. Sample size should not be determined in the mechanical manner envisaged in power analysis. It is inappropriate to criticize NHSTP for nonstatistical reasons. At the same time, neither effect size, nor confidence interval estimate, nor posterior probability can be used to exclude chance as an explanation of data. Neither can any of them fulfill the nonstatistical functions expected of them by critics.
Learning Node Embeddings in Interaction Graphs

Node embedding techniques have gained prominence since they produce continuous and low-dimensional features, which are effective for various tasks. Most existing approaches learn node embeddings by exploring the structure of networks and are mainly focused on static non-attributed graphs. However, many real-world applications, such as stock markets and public review websites, involve bipartite graphs with dynamic and attributed edges, called attributed interaction graphs. Different from conventional graph data, attributed interaction graphs involve two kinds of entities (e.g. investors/stocks and users/businesses) and edges of temporal interactions with attributes (e.g. transactions and reviews). In this paper, we study the problem of node embedding in attributed interaction graphs. Learning embeddings in interaction graphs is highly challenging due to the dynamics and heterogeneous attributes of edges. Different from conventional static graphs, in attributed interaction graphs, each edge can have totally different meanings when the interaction is at different times or associated with different attributes. We propose a deep node embedding method called IGE (Interaction Graph Embedding). IGE is composed of three neural networks: an encoding network is proposed to transform attributes into a fixed-length vector to deal with the heterogeneity of attributes; then encoded attribute vectors interact with nodes multiplicatively in two coupled prediction networks that investigate the temporal dependency by treating incident edges of a node as the analogy of a sentence in word embedding methods. The encoding network can be specifically designed for different datasets as long as it is differentiable, in which case it can be trained together with prediction networks by back-propagation. We evaluate our proposed method and various comparing methods on four real-world datasets. The experimental results prove the effectiveness of the learned embeddings by IGE on both node clustering and classification tasks.
Integrating Multiple Receptive Fields through Grouped Active Convolution

Convolutional networks have achieved great success in various vision tasks. This is mainly due to a considerable amount of research on network structure. In this study, instead of focusing on architectures, we focused on the convolution unit itself. The existing convolution unit has a fixed shape and is limited to observing restricted receptive fields. In earlier work, we proposed the active convolution unit (ACU), which can freely define its shape and learn by itself. In this paper, we provide a detailed analysis of the previously proposed unit and show that it is an efficient representation of a sparse weight convolution. Furthermore, we extend an ACU to a grouped ACU, which can observe multiple receptive fields in one layer. We found that the performance of a naive grouped convolution is degraded by increasing the number of groups; however, the proposed unit retains the accuracy even though the number of parameters decreases. Based on this result, we suggest a depthwise ACU, and various experiments have shown that our unit is efficient and can replace the existing convolutions.
PID control system analysis and design

With its three-term functionality offering treatment of both transient and steady-state responses, proportional-integral-derivative (PID) control provides a generic and efficient solution to real-world control problems. The wide application of PID control has stimulated and sustained research and development to "get the best out of PID", and "the search is on to find the next key technology or methodology for PID tuning". This article presents remedies for problems involving the integral and derivative terms. PID design objectives, methods, and future directions are discussed. Subsequently, a computerized simulation-based approach is presented, together with illustrative design results for first-order, higher order, and nonlinear plants. Finally, we discuss differences between academic research and industrial practice, so as to motivate new research directions in PID control.
Experience Replay for Real-Time Reinforcement Learning Control

Reinforcement-learning (RL) algorithms can automatically learn optimal control strategies for nonlinear, possibly stochastic systems. A promising approach for RL control is experience replay (ER), which learns quickly from a limited amount of data, by repeatedly presenting these data to an underlying RL algorithm. Despite its benefits, ER RL has been studied only sporadically in the literature, and its applications have largely been confined to simulated systems. Therefore, in this paper, we evaluate ER RL on real-time control experiments that involve a pendulum swing-up problem and the vision-based control of a goalkeeper robot. These real-time experiments are complemented by simulation studies and comparisons with traditional RL. As a preliminary, we develop a general ER framework that can be combined with essentially any incremental RL technique, and instantiate this framework for the approximate Q-learning and SARSA algorithms. The successful real-time learning results that are presented here are highly encouraging for the applicability of ER RL in practice.
The SIMC method for smooth PID controller tuning

The SIMC method for PID controller tuning (Skogestad 2003) has already found widespread industrial usage in Norway. This chapter gives an updated overview of the method, mainly from a user’s point of view. The basis for the SIMC method is a first-order plus time delay model, and we present a new effective method to obtain the model from a simple closed-loop experiment. An important advantage of the SIMC rule is that there is a single tuning parameter (τc) that gives a good balance between the PID parameters (Kc,τI ,τD), and which can be adjusted to get a desired trade-off between performance (“tight” control) and robustness (“smooth” control). Compared to the original paper of Skogestad (2003), the choice of the tuning parameter τc is discussed in more detail, and lower and upper limits are presented for tight and smooth tuning, respectively. Finally, the optimality of the SIMC PI rules is studied by comparing the performance (IAE) versus robustness (Ms) trade-off with the Pareto-optimal curve. The difference is small which leads to the conclusion that the SIMC rules are close to optimal. The only exception is for pure time delay processes, so we introduce the “improved” SIMC rule to improve the performance for this case. Chapter for PID book (planned: Springer, 2011,  R. Vilanova)  2011 Sigurd Skogestad Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Trondheim,  skoge@ntnu.no Chriss Grimholt Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Trondheim
Behavioural Computer Science: an agenda for combining modelling of human and system behaviours

The rapidly increasing pervasiveness and integration of computers in human society calls for a broad discipline under which this development can be studied. We argue that to design and use technology one needs to develop and use models of humans and machines in all their aspects, including cognitive and memory models, but also social influence and (artificial) emotions. We call this wider discipline Behavioural Computer Science (BCS), and argue in this paper for why BCS models should unify (models of) the behaviour of humans and machines when designing information and communication technology systems. Thus, one main point to be addressed is the incorporation of empirical evidence for actual human behaviour, instead of making inferences about behaviour based on the rational agent model. Empirical studies can be one effective way to constantly update the behavioural models. We are motivated by the future advancements in artificial intelligence which will give machines capabilities that from many perspectives will be indistinguishable from those of humans. Such machine behaviour would be studied using BCS models, looking at questions about machine trust like “Can a self driving car trust its passengers?”, or artificial influence like “Can the user interface adapt to the user’s behaviour, and thus influence this behaviour?”. We provide a few directions for approaching BCS, focusing on modelling of human and machine behaviour, as well as their interaction.
OFDM Based Real Time Video Transmission Using USRP

Development in the wireless communication systems is the evolving field of research in today’s world. The demand of high data rate, low latency at the minimum cost by the user requires many changes in the hardware organization. The use of digital modulation techniques like OFDM assures the reliability of communication in addition to providing flexibility and robustness. Modifications in the hardware structure can be replaced by the change in software only which gives birth to Software Define Radio (SDR): a radio which is more flexible as compared to conventional radio and can perform signal processing at the minimum cost. GNU Radio with the help of Universal Software Peripheral Radio (USRP) provides flexible and the cost effective SDR platform for the purpose of real time video transmission. The results given in this paper are taken from the experiment performed on USRP-1 along with the GNU Radio version 3.2.2.
FairGAN: Fairness-aware Generative Adversarial Networks

Fairness-aware learning is increasingly important in data mining. Discrimination prevention aims to prevent discrimination in the training data before it is used to conduct predictive analysis. In this paper, we focus on fair data generation that ensures the generated data is discrimination free. Inspired by generative adversarial networks (GAN), we present fairness-aware generative adversarial networks, called FairGAN, which are able to learn a generator producing fair data and also preserving good data utility. Compared with the naive fair data generation models, FairGAN further ensures the classifiers which are trained on generated data can achieve fair classification on real data. Experiments on a real dataset show the effectiveness of FairGAN.
IoT Middleware: A Survey on Issues and Enabling Technologies

The Internet of Things (IoT) provides the ability for humans and computers to learn and interact from billions of things that include sensors, actuators, services, and other Internet-connected objects. The realization of IoT systems will enable seamless integration of the cyber world with our physical world and will fundamentally change and empower human interaction with the world. A key technology in the realization of IoT systems is middleware, which is usually described as a software system designed to be the intermediary between IoT devices and applications. In this paper, we first motivate the need for an IoT middleware via an IoT application designed for real-time prediction of blood alcohol content using smartwatch sensor data. This is then followed by a survey on the capabilities of the existing IoT middleware. We further conduct a thorough analysis of the challenges and the enabling technologies in developing an IoT middleware that embraces the heterogeneity of IoT devices and also supports the essential ingredients of composition, adaptability, and security aspects of an IoT system.
Sustainable Business Strategies and PESTEL Framework

This paper aims to provide a brief review of cloud computing, followed by an analysis of cloud computing environment using the PESTEL framework. The future implications and limitations of adopting cloud computing as an effective eco-friendly strategy to reduce carbon footprint are also discussed in the paper. This paper concludes with a recommendation to guide researchers to further examine this phenomenon. Organizations today face tough economic times, especially following the recent global financial crisis and the evidence of catastrophic climate change. International and local businesses find themselves compelled to review their strategies. They need to consider their organizational expenses and priorities and to strategically consider how best to save. Traditionally, Information Technology (IT) department is one area that would be affected negatively in the review. Continuing to fund these strategic technologies during an economic downturn is vital to organizations. It is predicted that in coming years IT resources will only be available online. More and more organizations are looking at operating smarter businesses by investigating technologies such as cloud computing, virtualization and green IT to find ways to cut costs and increase efficiencies.
Neuroimaging creativity: A psychometric view

Many studies of creative cognition with a neuroimaging component now exist; what do they say about where and how creativity arises in the brain? We reviewed 45 brain-imaging studies of creative cognition. We found little clear evidence of overlap in their results. Nearly as many different tests were used as there were studies; this test diversity makes it impossible to interpret the different findings across studies with any confidence. Our conclusion is that creativity research would benefit from psychometrically informed revision, and the addition of neuroimaging methods designed to provide greater spatial localization of function. Without such revision in the behavioral measures and study designs, it is hard to see the benefit of imaging. We set out eight suggestions in a manifesto for taking creativity research forward.
2-2017 Lightweight data race detection for production runs

To detect data races that harm production systems, program analysis must target production runs. However, sound and precise data race detection adds too much run-time overhead for use in production systems. Even existing approaches that provide soundness or precision incur significant limitations. This work addresses the need for soundness (no missed races) and precision (no false races) by introducing novel, efficient production-time analyses that address each need separately. (1) Precise data race detection is useful for developers, who want to fix bugs but loathe false positives. We introduce a precise analysis called RaceChaser that provides low, bounded run-time overhead. (2) Sound race detection benefits analyses and tools whose correctness relies on knowledge of all potential data races. We present a sound, efficient approach called Caper that combines static and dynamic analysis to catch all data races in observed runs. RaceChaser and Caper are useful not only on their own; we introduce a framework that combines these analyses, using Caper as a sound filter for precise data race detection by RaceChaser. Our evaluation shows that RaceChaser and Caper are efficient and effective, and compare favorably with existing state-of-the-art approaches. These results suggest that RaceChaser and Caper enable practical data race detection that is precise and sound, respectively, ultimately leading to more reliable software systems.