nils-herrmann/clean_scirepeval huggingface dataset:
A Parallel Optimization of the Fast Algorithm of Convolution Neural Network on CPU

The application of convolutional neural network (CNN) is mainly based on the training over the big data, and it is necessary to obtain the relatively accurate values in real time. Although the GPU has several advantages over CPU, most of the clusters in mainstream parallel environments are composed of servers based on CPUs. So it is more useful to speed up the algorithms over the CPU-based clusters by parallelization. In this paper, an improved CNN algorithm based on Winogard algorithm was proposed. Firstly, we analyzed Winogard algorithm and found it has the best advantage in speed to small convolution kernel, such as 3×3. Then we increase the convolution process speed by reducing the complexity of convolution calculation. We also discussed the parallel implementation and optimization to the improved Winogard algorithm on CPU-based cluster. Finally, we compared the computing efficiency of different numbers of computing cores in the Intel Xeon Phi CPU platform and the results show that the best parallelism is about 6 TFLOPS.
Process Modeling

Business process reengineering-the redesign of an organization's business processes to make them more efficient. • Coordination technology-an aid to managing dependencies among the agents within a business process, and provides automated support for the most routinized component processes.  Process-driven software development environments-an automated system for integrating the work of all software related management and staff; it provides embedded support for an orderly and defined software development process. These three applications share a growing requirement to represent the processes through which work is accomplished. To the extent that automation is involved, process representation becomes a vital issue in redesigning work and allocating responsibilities between humans and computers. This requirement reflects the growing use of distributed , networked systems to link the interacting agents responsible for executing a business process. To establish process modeling as a unique area, researchers must identify conceptual boundaries that distinguish their work from model-ing in other areas of information science. Process modeling is distinguished from other types of model-ing in computer science because many of the phenomena modeled must be enacted by a human rather than a machine. At least some mod-eling, however, in the area of human-machine system integration or information systems design has this 'human-executable' attribute. Rather than focusing solely on the user's behavior at the interface or the flow and transformation of data within the system, process model-ing also focuses on interacting behaviors among agents, regardless of whether a computer is involved in the transactions. Much of the research on process modeling has been conducted on software development organizations , since the software engineering community is already accustomed to formal modeling. Software process modeling, in particular , explicitly focuses on phenomena that occur during software creation and evolution, a domain different from that usually mod-eled in human-machine integration or information systems design. Software development is a challenging focus for process modeling because of the creative problem-solving involved in requirements analysis and design, and the coordination of team interactions during the development of a complex intellectual artifact. In this article, software process modeling will be used as an example application for describing the current status of process modeling, issues for practical use, and the research questions that remain ahead. Most software organizations possess several yards of software life cycle description, enough to wrap endlessly around the walls of project rooms. Often these descriptions do not correspond to the processes actually performed during software …
Student teachers' thinking processes and ICT integration: Predictors of prospective teaching behaviors with educational technology

Teacher candidates should be prepared to integrate information and communication technology (ICT) into their future teaching practices. Despite the increased availability and support for ICT integration, relatively few teachers intend to integrate ICT into their teaching activities. The available research has thus far focused on isolated teacher related variables to explain the weak level of ICT integration. Also, most of this research was set up in Western settings. The present study centers on the impact of Chinese student teachers' gender, constructivist teaching beliefs, teaching self-efficacy, computer self-efficacy, and computer attitudes on their prospective ICT use. For this purpose, a survey was set up involving teacher education college students of four teacher education universities in China (N = 727). Results showed that prospective ICT integration significantly correlates with all teacher related variables. Building on a path analysis, prospective ICT integration could be directly predicted on the base of teacher thinking variables (constructivist teaching beliefs, teacher self-efficacy, computer self-efficacy and computer attitudes in education), and indirectly by the gender of the student teachers. Implications for teacher education and further research are discussed.
Automatic Learning of Combat Models for RTS Games

Game tree search algorithms, such as Monte Carlo Tree Search (MCTS), require access to a forward model (or “simulator”) of the game at hand. However, in some games such forward model is not readily available. In this paper we address the problem of automatically learning forward models (more specifically, combats models) for two-player attrition games. We report experiments comparing several approaches to learn such combat model from replay data to models generated by hand. We use StarCraft, a Real-Time Strategy (RTS) game, as our application domain. Specifically, we use a large collection of already collected replays, and focus on learning a combat model for tactical combats.
Comparative Study of GAN and VAE

Generative models are very popular in a field of unsupervised learning.They are tremendously successful to learn underlying data distribution of training data and generate a new data with some variations.This paper presents a detailed study of generative models and how they differ from traditional discriminative models.The paper more focus on two most popular generative models such as Variational Autoencoder(VAE) and Generative Adversarial Network(GAN).The paper includes working of these generative models, their architecture and an experiment is conducted to generate images using very popular MNIST data set.The comparison between these two models and their advantages and disadvantages are presented based on an experiment. At last, some solutions are presented to further improve these models.
Polymorph: A Model for Dynamic Level Generation

Players begin games at different skill levels and develop their skill at different rates—so that even the best-designed games are uninterestingly easy for some players and frustratingly difficult for others. A proposed answer to this challenge is Dynamic Difficulty Adjustment (DDA), a general category of approaches that alter games during play, in response to player performance. However, nearly all these techniques are focused on basic parameter tweaking, while the difficulty of many games is connected to aspects that are more challenging to adjust dynamically, such as level design. Further, most DDA techniques are based on designer intuition, which may not reflect actual play patterns. Responding to these challenges, we have created Polymorph, which employs techniques from level generation and machine learning to understand level difficulty and player skill, dynamically constructing levels for a 2D platformer game with continually-appropriate challenge. We present the results of the user study on which Polymorph's model of level difficulty is based, as well as a discussion of the unique features of the model. We believe Polymorph creates a play experience that is unique because the changes are both personalized and structural, while also providing an example of a new application of machine learning to aid game design.
A General Framework for Object Detection

This paper presents a general trainable framework for object detection in static images of cluttered scenes. The detection technique we develop is based on a wavelet representation of an object class derived from a statistical analysis of the class instances. By learning an object class in terms of a subset of an overcomplete dictionary of wavelet basis functions, we derive a compact representation of an object class which is used as an input to a suppori vector machine classifier. This representation overcomes both the problem of in-class variability and provides a low false detection rate in unconstrained environments. We demonstrate the capabilities of the technique i n two domains whose inherent information content differs significantly. The first system is face detection and the second is the domain of people which, in contrast to faces, vary greatly in color, texture, and patterns. Unlike previous approaches, this system learns from examples and does not rely on any a priori (handcrafted) models or motion-based segmentation. The paper also presents a motion-based extension to enhance the performance of the detection algorithm over video sequences. The results presented here suggest that this architecture may well be quite general.
MARKETING MIX AND CONSUMER BEHAVIOR

Marketing mix is the combination of strategies and activities that companies use to sell their goods and services. By focusing on the people who buy, a company can pick the right features for the product, the right price and distribution outlets, and the right words and approaches for promoting the product. The entire success of the company lies in the amount of belief and loyalty; its customers are having in it. Consumer behavior analysis is an important domain for a marketing manager as it gives insight into a number of factors which affect sales and relationships that are not in the control of a company. What is in control of a manager is the marketing mix. The marketing mix allows a marketer to better relate to the identified target market; based on the characteristics of the target market, the marketer will “build” the marketing mix elements around the target market.
Non-local Neural Networks

Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies. Inspired by the classical non-local means method  in computer vision, our non-local operation computes the response at a position as a weighted sum of the features at all positions. This building block can be plugged into many computer vision architectures. On the task of video classification, even without any bells and whistles, our nonlocal models can compete or outperform current competition winners on both Kinetics and Charades datasets. In static image recognition, our non-local models improve object detection/segmentation and pose estimation on the COCO suite of tasks. Code will be made available.
Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks

In the field of agricultural information, the automatic identification and diagnosis of maize leaf diseases is highly desired. To improve the identification accuracy of maize leaf diseases and reduce the number of network parameters, the improved GoogLeNet and Cifar10 models based on deep learning are proposed for leaf disease recognition in this paper. Two improved models that are used to train and test nine kinds of maize leaf images are obtained by adjusting the parameters, changing the pooling combinations, adding dropout operations and rectified linear unit functions, and reducing the number of classifiers. In addition, the number of parameters of the improved models is significantly smaller than that of the VGG and AlexNet structures. During the recognition of eight kinds of maize leaf diseases, the GoogLeNet model achieves a top - 1 average identification accuracy of 98.9%, and the Cifar10 model achieves an average accuracy of 98.8%. The improved methods are possibly improved the accuracy of maize leaf disease, and reduced the convergence iterations, which can effectively improve the model training and recognition efficiency.
Mechatronic systems — Innovative products with embedded control

Many technical processes and products in the area of mechanical and electrical engineering are showing an increasing integration of mechanics with digital electronics and information processing. This integration is between the components (hardware) and the information-driven functions (software), resulting in integrated systems called mechatronic systems. Their development involves finding an optimal balance between the basic mechanical structure, sensor and actuator implementation, automatic information processing and overall control. Simultaneous design of mechanics and electronics, hardware and software and embedded control functions resulting in an integrated component or system are all of major importance. This technical progress has a very large influence on a multitude of products in the area of mechanical, electrical and electronic engineering and changes the design, for example, of conventional electromechanical components, machines, vehicles and precision mechanical devices with increasing intensity. This contribution summarizes ongoing developments for mechatronic systems, shows design approaches and examples of mechatronic products and considers various embedded control functions and system’s integrity. One field of ongoing developments, automotive mechatronics, where especially large influences can be seen, is described in more detail by discussing mechatronic suspensions, mechatronic brakes, active steering and roll stabilization systems. r 2007
Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks

While there have been significant advances in detecting emotions from speech and image recognition, emotion detection on text is still under-explored and remained as an active research field. This paper introduces a corpus for text-based emotion detection on multiparty dialogue as well as deep neural models that outperform the existing approaches for document classification. We first present a new corpus that provides annotation of seven emotions on consecutive utterances in dialogues extracted from the show, Friends. We then suggest four types of sequence-based convolutional neural network models with attention that leverage the sequence information encapsulated in dialogue. Our best model shows the accuracies of 37.9% and 54% for fineand coarsegrained emotions, respectively. Given the difficulty of this task, this is promising.
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm

In this paper we adopt general sum stochas tic games as a framework for multiagent re inforcement learning Our work extends pre vious work by Littman on zero sum stochas tic games to a broader framework We de sign a multiagent Q learning method under this framework and prove that it converges to a Nash equilibrium under speci ed condi tions This algorithm is useful for nding the optimal strategy when there exists a unique Nash equilibrium in the game When there exist multiple Nash equilibria in the game this algorithm should be combined with other learning techniques to nd optimal strategies
Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon

This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-art methodologies involve algorithmic decisions that either require too much computing time or are not mathematically well defined. Thus, machine learning looks like a promising candidate to effectively deal with those decisions. We advocate for pushing further the integration of machine learning and combinatorial optimization and detail methodology to do so. A main point of the paper is seeing generic optimization problems as data points and inquiring what is the relevant distribution of problems to use for learning on a given task.
Evaluating the Effect of Using Physical Manipulatives to Foster Computational Thinking in Elementary School

Researchers and educators have designed curricula and resources for introductory programming environments such as Scratch, App Inventor, and Kodu to foster computational thinking in K-12. This paper is an empirical study of the effectiveness and usefulness of tiles and flashcards developed for Microsoft Kodu Game Lab to support students in learning how to program and develop games. In particular, we investigated the impact of physical manipulatives on 3rd -- 5th grade students' ability to understand, recognize, construct, and use game programming design patterns. We found that the students who used physical manipulatives performed well in rule construction, whereas the students who engaged more with the rule editor of the programming environment had better mental simulation of the rules and understanding of the concepts.
Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power

Experimental analysis of the performance of a proposed method is a crucial and necessary task in an investigation. In this paper, we focus on the use of nonparametric statistical inference for analyzing the results obtained in an experiment design in the field of computational intelligence. We present a case study which involves a set of techniques in classification tasks and we study a set of nonparametric procedures useful to analyze the behavior of a method with respect to a set of algorithms, such as the framework in which a new proposal is developed. Particularly, we discuss some basic and advanced nonparametric approaches which improve the results offered by the Friedman test in some circumstances. A set of post hoc procedures for multiple comparisons is presented together with the computation of adjusted p-values. We also perform an experimental analysis for comparing their power, with the objective of detecting the advantages and disadvantages of the statistical tests described. We found that some aspects such as the number of algorithms, number of data sets and differences in performance offered by the control method are very influential in the statistical tests studied. Our final goal is to offer a complete guideline for the use of nonparametric statistical procedures for performing multiple comparisons in experimental studies.
Wireless IoT based metering system for energy efficient smart cites

The emerging of IoT concept recently in our lives, has offered the chance to establish energy efficient smart devices, systems and cities. Due to the urging need for conserving energy, this paper proposes an IoT based energy efficient wireless smart metering system design. It competes with the existing meters as being a low cost completely integrated metering system. It offers an easily operated Android application for users as well as a Website and database for the electricity supplier company. The proposed system design has an accuracy level of 97% and it is about 25% lower cost than its peer in the global market. The proposed design reduced the power consumption by 16%.
Detraining produces minimal changes in physical performance and hormonal variables in recreationally strength-trained men.

The object of this study was to examine changes in muscular strength, power, and resting hormonal concentrations during 6 weeks of detraining (DTR) in recreationally strength-trained men. Each subject was randomly assigned to either a DTR (n = 9) or resistance training (RT; n = 7) group after being matched for strength, body size, and training experience. Muscular strength and power testing, anthropometry, and blood sampling were performed before the experimental period (T1), after 3 weeks (T2), and after the 6-week experimental period (T3). One-repetition maximum (1RM) shoulder and bench press increased in RT at T3 (p </= 0.05), whereas no significant changes were observed in DTR. Peak power output and mean power output significantly decreased (9 and 10%) in DTR at T2. Peak torque of the elbow flexors at 90 degrees did not change in the RT group but did significantly decrease by 11.9% at T3 compared with T1 in the DTR group. Vertical jump height increased in RT at T2 but did not change in DTR. Neither group displayed any changes in 1RM squat, body mass, percent body fat, or resting concentrations of growth hormone, follicle-stimulating hormone, luteinizing hormone, sex hormone-binding globulin, testosterone, cortisol, or adrenocorticotropin. These data demonstrate that 6 weeks of resistance DTR in recreationally trained men affects power more than it does strength without any accompanying changes in resting hormonal concentrations. For the recreational weight trainer, losses in strength over 6 weeks are less of a concern compared with anaerobic power and upper arm isometric force production. Anaerobic power exercise with a high metabolic component coming from glycolysis might be of importance for reducing the impact of DTR on Wingate power performances. A minimal maintenance training program is recommended for the recreational lifter to offset any reductions in performance.
Framewise phoneme classification with bidirectional LSTM networks

In this paper, we apply bidirectional training to a long short term memory (LSTM) network for the first time. We also present a modified, full gradient version of the LSTM learning algorithm. We discuss the significance of framewise phoneme classification to continuous speech recognition, and the validity of using bidirectional networks for online causal tasks. On the TIMIT speech database, we measure the framewise phoneme classification scores of bidirectional and unidirectional variants of both LSTM and conventional recurrent neural networks (RNNs). We find that bidirectional LSTM outperforms both RNNs and unidirectional LSTM.
In the zone or zoning out? Tracking behavioral and neural fluctuations during sustained attention.

Despite growing recognition that attention fluctuates from moment-to-moment during sustained performance, prevailing analysis strategies involve averaging data across multiple trials or time points, treating these fluctuations as noise. Here, using alternative approaches, we clarify the relationship between ongoing brain activity and performance fluctuations during sustained attention. We introduce a novel task (the gradual onset continuous performance task), along with innovative analysis procedures that probe the relationships between reaction time (RT) variability, attention lapses, and intrinsic brain activity. Our results highlight 2 attentional states-a stable, less error-prone state ("in the zone"), characterized by higher default mode network (DMN) activity but during which subjects are at risk of erring if DMN activity rises beyond intermediate levels, and a more effortful mode of processing ("out of the zone"), that is less optimal for sustained performance and relies on activity in dorsal attention network (DAN) regions. These findings motivate a new view of DMN and DAN functioning capable of integrating seemingly disparate reports of their role in goal-directed behavior. Further, they hold potential to reconcile conflicting theories of sustained attention, and represent an important step forward in linking intrinsic brain activity to behavioral phenomena.