nils-herrmann/clean_scirepeval huggingface dataset:
Segmentation of stock trading customers according to potential value

In this article, we use three clustering methods (K-means, self-organizing map, and fuzzy K-means) to find properly graded stock market brokerage commission rates based on the 3-month long total trades of two different transaction modes (representative assisted and online trading system). Stock traders for both modes are classified in terms of the amount of the total trade as well as the amount of trade of each transaction mode, respectively. Results of our empirical analysis indicate that fuzzy K-means cluster analysis is the most robust approach for segmentation of customers of both transaction modes. We then propose a decision tree based rule to classify three groups of customers and suggest different brokerage commission rates of 0.4, 0.45, and 0.5% for representative assisted mode and 0.06, 0.1, and 0.18% for online trading system, respectively. q 2003
All-digital TX frequency synthesizer and discrete-time receiver for Bluetooth radio in 130-nm CMOS

We present a single-chip fully compliant Bluetooth radio fabricated in a digital 130-nm CMOS process. The transceiver is architectured from the ground up to be compatible with digital deep-submicron CMOS processes and be readily integrated with a digital baseband and application processor. The conventional RF frequency synthesizer architecture, based on the voltage-controlled oscillator and the phase/frequency detector and charge-pump combination, has been replaced with a digitally controlled oscillator and a time-to-digital converter, respectively. The transmitter architecture takes advantage of the wideband frequency modulation capability of the all-digital phase-locked loop with built-in automatic compensation to ensure modulation accuracy. The receiver employs a discrete-time architecture in which the RF signal is directly sampled and processed using analog and digital signal processing techniques. The complete chip also integrates power management functions and a digital baseband processor. Application of the presented ideas has resulted in significant area and power savings while producing structures that are amenable to migration to more advanced deep-submicron processes, as they become available. The entire IC occupies 10 mm/sup 2/ and consumes 28 mA during transmit and 41 mA during receive at 1.5-V supply.
An automated FX trading system using adaptive reinforcement learning

This paper introduces adaptive reinforcement learning (ARL) as the basis for a fully automated trading system application. The system is designed to trade foreign exchange (FX) markets and relies on a layered structure consisting of a machine learning algorithm, a risk management overlay and a dynamic utility optimization layer. An existing machine-learning method called recurrent reinforcement learning (RRL) was chosen as the underlying algorithm for ARL. One of the strengths of our approach is that the dynamic optimization layer makes a fixed choice of model tuning parameters unnecessary. It also allows for a risk-return trade-off to be made by the user within the system. The trading system is able to make consistent gains out-of-sample while avoiding large draw-downs. q 2005
Social cognition, artefacts, and stigmergy: A comparative analysis of theoretical frameworks for the understanding of artefact-mediated collaborative activity

Collective behaviour is often characterised by the so-called “coordination paradox” : Looking at individual ants, for example, they do not seem to cooperate or communicate explicitly, but nevertheless at the social level cooperative behaviour, such as nest building, emerges, apparently without any central coordination. In the case of social insects such emergent coordination has been explained by the theory of stigmergy, which describes how individuals can effect the behaviour of others (and their own) through artefacts, i.e. the product of their own activity (e.g., building material in the ants’ case). Artefacts clearly also play a strong role in human collective behaviour, which has been emphasised, for example, by proponents of activity theory and distributed cognition. However, the relation between theories of situated/social cognition and theories of social insect behaviour has so far received relatively li ttle attention in the cognitive science literature. This paper aims to take a step in this direction by comparing three theoretical frameworks for the study of cognition in the context of agent-environment interaction (activity theory, situated action, and distributed cognition) to each other and to the theory of stigmergy as a possible minimal common ground. The comparison focuses on what each of the four theories has to say about the role/nature of (a) the agents involved in collective behaviour, (b) their environment, (c) the collective activities addressed, and (d) the role that artefacts play in the interaction between agents and their environments, and in particular in the coordination
Delegate the smartphone user? Security awareness in smartphone platforms

Smartphone users increasingly download and install third-party applications from official application repositories. Attackers may use this centralized application delivery architecture as a security and privacy attack vector. This risk increases since application vetting mechanisms are often not in place and the user is delegated to authorize which functionality and protected resources are accessible by third-party applications. In this paper, we mount a survey to explore the security awareness of smartphone users who download applications from official application repositories (e.g. Google Play, Apple’s App Store, etc.). The survey findings suggest a security complacency, as the majority of users trust the app repository, security controls are not enabled or not added, and users disregard security during application selection and installation. As a response to this security complacency, we built a prediction model to indentify users who trust the app repository. Then, the model is assessed, evaluated, and proved to be statistically significant and efficient.
Three-Dimensional Printing of Prosthetic Hands for Children.

Children with hand reductions, whether congenital or traumatic, have unique prosthetic needs. They present a challenge because of their continually changing size due to physical growth as well as changing needs due to psychosocial development. Conventional prosthetics are becoming more technologically advanced and increasingly complex. Although these are welcome advances for adults, the concomitant increases in weight, moving parts, and cost are not beneficial for children. Pediatric prosthetic needs may be better met with simpler solutions. Three-dimensional printing can be used to fabricate rugged, light-weight, easily replaceable, and very low cost assistive hands for children.
Detecting and tracking people in real time with RGB-D camera

We propose a novel approach to automatic detection and tracking of people taking different poses in cluttered and dynamic environments using a single RGB-D camera. The original RGB-D pixels are transformed to a novel point ensemble image (PEI), and we demonstrate that human detection and tracking in 3D space can be performed very effectively with this new representation. The detector in the first phase quickly locates human physiquewise plausible candidates, which are then further carefully filtered in a supervised learning and classification second phase. Joint statistics of color and height are computed for data association to generate final 3D motion trajectories of tracked individuals. Qualitative and quantitative experimental results obtained on the publicly available office dataset, mobile camera dataset and the real-world clothing store dataset we created show very promising results.
Ball tracking and goal detection for middle size soccer robot using omnidirectional camera

The RoboCup Middle Size League competition is a standard real-world test bed for autonomous multi-robot control, robot vision and other relative research subjects. In the past decade, omnidirectional vision system has become one of the most important sensors for the RoboCup soccer robots, for it can provide a 360° view of the robot's surrounding environment in a single image. The robot can use it for tracking and self-localization which very important for robot's control, strategy, and coordination. This paper will discuss the vision system to detect ball, goals, and calculate the angle and real distance from those objects. Based on the research that has been done, the system can detect the ball and the goal, and calculate the angle and the actual distance with a maximum error distance is 5%.
Context-Aware Computing: The CyberDesk Project

The CyberDesk project is aimed at providing a software architecture that dynamically integrates software modules. This integration is driven by a user’s context, where context includes the user’s physical, social, emotional, and mental (focus-of-attention) environments. While a user’s context changes in all settings, it tends to change most frequently in a mobile setting. We have used the CyberDesk ystem in a desktop setting and are currently using it to build an intelligent home nvironment.
Facile synthesis of soluble graphene via a green reduction of graphene oxide in tea solution and its biocomposites.

The chemical reduction of graphene oxide (GO) typically involves highly toxic reducing agents that are harmful to human health and environment, and complicated surface modification is often needed to avoid aggregation of the reduced GO during reduction process. In this paper, a green and facile strategy is reported for the fabrication of soluble reduced GO. The proposed method is based on the reduction of exfoliated GO in green tea solution by making use of the reducing capability and the aromatic rings of tea polyphenol (TP) that contained in tea solution. The measurements of the resultant graphene confirm the efficient removal of the oxygen-containing groups in GO. The strong interactions between the reduced graphene and the aromatic TPs guarantee the good dispersion of the reduced graphene in both aqueous and a variety of organic solvents. These features endow this green approach with great potential in constructing of various graphene-based materials, especially for high-performance biorelated materials as demonstrated in this study of chitosan/graphene composites.
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning

One of the main concerns of deep reinforcement learning (DRL) is the data inefficiency problem, which stems both from an inability to fully utilize data acquired and from naive exploration strategies. In order to alleviate these problems, we propose a DRL algorithm that aims to improve data efficiency via both the utilization of unrewarded experiences and the exploration strategy by combining ideas from unsupervised auxiliary tasks, intrinsic motivation, and hierarchical reinforcement learning (HRL). Our method is based on a simple HRL architecture with a metacontroller and a subcontroller. The subcontroller is intrinsically motivated by the metacontroller to learn to control aspects of the environment, with the intention of giving the agent: 1) a neural representation that is generically useful for tasks that involve manipulation of the environment and 2) the ability to explore the environment in a temporally extended manner through the control of the metacontroller. In this way, we reinterpret the notion of pixel- and feature-control auxiliary tasks as reusable skills that can be learned via an intrinsic reward. We evaluate our method on a number of Atari 2600 games. We found that it outperforms the baseline in several environments and significantly improves performance in one of the hardest games--Montezuma's revenge--for which the ability to utilize sparse data is key. We found that the inclusion of intrinsic reward is crucial for the improvement in the performance and that most of the benefit seems to be derived from the representations learned during training.
Implementation of the Daum-Huang exact-flow particle filter

Several versions of the Daum-Huang (DH) filter have been introduced recently to address the task of discrete-time nonlinear filtering. The filters propagate a particle set over time to track the system state, but, in contrast to conventional particle filters, there is no proposal density or importance sampling involved. Particles are smoothly migrated using a particle flow derived from a log-homotopy relating the prior and the posterior. Impressive performance has been demonstrated for a wide range of systems, but the implemented algorithms rely on an extended/unscented Kalman filter (EKF/UKF) that is executed in parallel. We illustrate through simulation that the performance of the exact flow DH filter can be compromised when the UKF and EKF fail. By introducing simple but important modifications to the exact flow DH filter implementation, the performance can be improved dramatically.
Springrobot: a prototype autonomous vehicle and its algorithms for lane detection

This work presents the current status of the Springrobot autonomous vehicle project, whose main objective is to develop a safety-warning and driver-assistance system and an automatic pilot for rural and urban traffic environments. This system uses a high precise digital map and a combination of various sensors. The architecture and strategy for the system are briefly described and the details of lane-marking detection algorithms are presented. The R and G channels of the color image are used to form graylevel images. The size of the resulting gray image is reduced and the Sobel operator with a very low threshold is used to get a grayscale edge image. In the adaptive randomized Hough transform, pixels of the gray-edge image are sampled randomly according to their weights corresponding to their gradient magnitudes. The three-dimensional (3-D) parametric space of the curve is reduced to the two-dimensional (2-D) and the one-dimensional (1-D) space. The paired parameters in two dimensions are estimated by gradient directions and the last parameter in one dimension is used to verify the estimated parameters by histogram. The parameters are determined coarsely and quantization accuracy is increased relatively by a multiresolution strategy. Experimental results in different road scene and a comparison with other methods have proven the validity of the proposed method.
Customer Loyalty Explained by Electronic Recovery Service Quality : Implications of the Customer Relationship Re-Establishment for Consumer Electronics E-Tailers

This non-experimental, causal study related to examine and explore the relationships among electronic service quality, customer satisfaction, electronics recovery service quality, and customer loyalty for consumer electronics e-tailers. This study adopted quota and snowball sampling. A total of 121 participants completed the online survey. Out of seven hypotheses in this study, five were supported, whereas two were not supported. Findings indicated that electronic recovery service quality had positive effect on customer loyalty. However, findings indicated that electronic recovery service quality had no effect on perceived value and customer satisfaction. Findings also indicated that perceived value and customer satisfaction were two significant variables that mediated the relationships between electronic service quality and customer loyalty. Moreover, this study found that electronic service quality had no direct effect on customer satisfaction, but had indirect positive effects on customer satisfaction for consumer electronics e-tailers. In terms of practical implications, consumer electronics e-tailers' managers could formulate a competitive strategy based on the modified Electronic Customer Relationship Re-Establishment model to retain current customers and to enhance customer relationship management (CRM). The limitations and recommendations for future research were also included in this study.
Protein Family Classification with Neural Networks

Understanding protein function from amino acid sequence is a fundamental problem in biology. In this project, we explore how well we can represent biological function through examination of raw sequence alone. Using a large corpus of protein sequences and their annotated protein families, we learn dense vector representations for amino acid sequences using the co-occurrence statistics of short fragments. Then, using this representation, we experiment with several neural network architectures to train classifiers for protein family identification. We show good performance for a multi-class prediction problem with 589 protein family classes.
Conditional Generative Adversarial Network for Structured Domain Adaptation

In recent years, deep neural nets have triumphed over many computer vision problems, including semantic segmentation, which is a critical task in emerging autonomous driving and medical image diagnostics applications. In general, training deep neural nets requires a humongous amount of labeled data, which is laborious and costly to collect and annotate. Recent advances in computer graphics shed light on utilizing photo-realistic synthetic data with computer generated annotations to train neural nets. Nevertheless, the domain mismatch between real images and synthetic ones is the major challenge against harnessing the generated data and labels. In this paper, we propose a principled way to conduct structured domain adaption for semantic segmentation, i.e., integrating GAN into the FCN framework to mitigate the gap between source and target domains. Specifically, we learn a conditional generator to transform features of synthetic images to real-image like features, and a discriminator to distinguish them. For each training batch, the conditional generator and the discriminator compete against each other so that the generator learns to produce real-image like features to fool the discriminator; afterwards, the FCN parameters are updated to accommodate the changes of GAN. In experiments, without using labels of real image data, our method significantly outperforms the baselines as well as state-of-the-art methods by 12% ~ 20% mean IoU on the Cityscapes dataset.
Frequency control and wind turbine technologies

Increasing levels of wind generation has resulted in an urgent need for the assessment of their impact on frequency control of power systems. Whereas increased system inertia is intrinsically linked to the addition of synchronous generation to power systems, due to differing electromechanical characteristics, this inherent link is not present in wind turbine generators. Regardless of wind turbine technology, the displacement of conventional generation with wind will result in increased rates of change of system frequency. The magnitude of the frequency excursion following a loss of generation may also increase. Amendment of reserve policies or modification of wind turbine inertial response characteristics may be necessary to facilitate increased levels of wind generation. This is particularly true in small isolated power systems.
A Comparative Study of CPU Scheduling Algorithms

Developing CPU scheduling algorithms and understanding their impact in practice can be difficult and time consuming due to the need to modify and test operating system kernel code and measure the resulting performance on a consistent workload of real applications. As processor is the important resource, CPU scheduling becomes very important in accomplishing the operating system (OS) design goals. The intention should be allowed as many as possible running processes at all time in order to make best use of CPU. This paper presents a state diagram that depicts the comparative study of various scheduling algorithms for a single CPU and shows which algorithm is best for the particular situation. Using this representation, it becomes much easier to understand what is going on inside the system and why a different set of processes is a candidate for the allocation of the CPU at different time. The objective of the study is to analyze the high efficient CPU scheduler on design of the high quality scheduling algorithms which suits the scheduling goals.
Predicting Grammaticality on an Ordinal Scale

Automated methods for identifying whether sentences are grammatical have various potential applications (e.g., machine translation, automated essay scoring, computer-assisted language learning). In this work, we construct a statistical model of grammaticality using various linguistic features (e.g., misspelling counts, parser outputs, n-gram language model scores). We also present a new publicly available dataset of learner sentences judged for grammaticality on an ordinal scale. In evaluations, we compare our system to the one from Post (2011) and find that our approach yields state-of-the-art performance.
Kernel-based feature extraction with a speech technology application

Kernel-based nonlinear feature extraction and classification algorithms are a popular new research direction in machine learning. This paper examines their applicability to the classification of phonemes in a phonological awareness drilling software package. We first give a concise overview of the nonlinear feature extraction methods such as kernel principal component analysis (KPCA), kernel independent component analysis (KICA), kernel linear discriminant analysis (KLDA), and kernel springy discriminant analysis (KSDA). The overview deals with all the methods in a unified framework, regardless of whether they are unsupervised or supervised. The effect of the transformations on a subsequent classification is tested in combination with learning algorithms such as Gaussian mixture modeling (GMM), artificial neural nets (ANN), projection pursuit learning (PPL), decision tree-based classification (C4.5), and support vector machines (SVMs). We found, in most cases, that the transformations have a beneficial effect on the classification performance. Furthermore, the nonlinear supervised algorithms yielded the best results.
The effect of organizational support on ERP implementation

The purpose of this paper is to propose a model to examine the impact of organizational support on behavioral intention (BI) regarding enterprise resource planning (ERP) implementation based on the technology acceptance model (TAM).  A research model is proposed which describes the effects of organizational support, both formal and informal, on factors of TAM. A survey questionnaire is developed to test the proposed model. A total of 700 of questionnaires are distributed to users in small and medium enterprises that have implemented ERP systems in Korea and 209 responses are used for analyses. Structural equation modeling is employed to test the research hypotheses.  The results indicate that the organizational support is an important factor for perceived usefulness (PU) and perceived ease of use (PEOU). PU and PEOU seem to lead to a higher level of interest in the ERP system and BI to use the system. The most notable finding of our study is that organizational support is positively associated with factors of TAM.  The survey data used in this paper are collected from smalland medium-sized companies in South Korea. Thus, the respondents in these firms might have been trained at different levels or on different modules of ERP, which would yield diversity in subject experience with different ERP systems.  To improve the efficiency and effectiveness of ERP implementation in a real world environment, organizations need to better understand user satisfaction. The TAM model provides a theoretical construct to explain how user satisfaction is affected.