nils-herrmann/clean_scirepeval huggingface dataset:
The evolving theory of quality management : The role of Six Sigma

While Six Sigma is increasingly implemented in industry, little academic research has been done on Six Sigma and its influence on quality management theory and application. There is a criticism that Six Sigma simply puts traditional quality management practices in a new package. To investigate this issue and the role of Six Sigma in quality management, this study reviewed both the traditional quality management and Six Sigma literatures and identified three new practices that are critical for implementing Six Sigma’s concept and method in an organization. These practices are referred to as: Six Sigma role structure, Six Sigma structured improvement procedure, and Six Sigma focus on metrics. A research model and survey instrument were developed to investigate how these Six Sigma practices integrate with seven traditional quality management practices to affect quality performance and business performance. Test results based on a sample of 226 US manufacturing plants revealed that the three Six Sigma practices are distinct practices from traditional quality management practices, and that they complement the traditional quality management practices in improving performance. The implications of the findings for researchers and practitioners are discussed and further research directions are offered.
Facebook Friends with (Health) Benefits? Exploring Social Network Site Use and Perceptions of Social Support, Stress, and Well-Being

There is clear evidence that interpersonal social support impacts stress levels and, in turn, degree of physical illness and psychological well-being. This study examines whether mediated social networks serve the same palliative function. A survey of 401 undergraduate Facebook users revealed that, as predicted, number of Facebook friends associated with stronger perceptions of social support, which in turn associated with reduced stress, and in turn less physical illness and greater well-being. This effect was minimized when interpersonal network size was taken into consideration. However, for those who have experienced many objective life stressors, the number of Facebook friends emerged as the stronger predictor of perceived social support. The "more-friends-the-better" heuristic is proposed as the most likely explanation for these findings.
Bio-Heat Transfer Model of Deep Brain Stimulation Induced Temperature changes

There is a growing interest in the use of chronic deep brain stimulation (DBS) for the treatment of medically refractory movement disorders and other neurological and psychiatric conditions. Fundamental questions remain about the physiologic effects and safety of DBS. Previous basic research studies have focused on the direct polarization of neuronal membranes by electrical stimulation. The goal of this paper is to provide information on the thermal effects of DBS using finite element models to investigate the magnitude and spatial distribution of DBS induced temperature changes. The parameters investigated include: stimulation waveform, lead selection, brain tissue electrical and thermal conductivity, blood perfusion, metabolic heat generation during the stimulation. Our results show that clinical deep brain stimulation protocols will increase the temperature of surrounding tissue by up to 0.8degC depending on stimulation/tissue parameters
Influence Factors of Understanding Business Process Models

The increasing utilization of business process models both in business analysis and information systems development raises several issues regarding quality measures. In this context, this paper discusses understandability as a particular quality aspect and its connection with personal, model, and content related factors. We use an online survey to explore the ability of the model reader to draw correct conclusions from a set of process models. For the first group of the participants we used models with abstract activity labels (e.g. A, B, C) while the second group received the same models with illustrative labels such as “check credit limit”. The results suggest that all three categories indeed have an impact on the understandability.
Approaches for a tether-guided landing of an autonomous helicopter

In this paper, we address the design of an autopilot for autonomous landing of a helicopter on a rocking ship, due to rough sea. A tether is used for landing and securing a helicopter to the deck of the ship in rough weather. A detailed nonlinear dynamic model for the helicopter is used. This model is underactuated, where the rotational motion couples into the translation. This property is used to design controllers which separate the time scales of rotation and translation. It is shown that the tether tension can be used to couple the translation of the helicopter to the rotation. Two controllers are proposed in this paper. In the first, the rotation time scale is chosen much shorter than the translation, and the rotation reference signals are created to achieve a desired controlled behavior of the translation. In the second, due to coupling of the translation of the helicopter to the rotation through the tether, the translation reference rates are created to achieve a desired controlled behavior of the attitude and altitude. Controller A is proposed for use when the helicopter is far away from the goal, while Controller B is for the case when the helicopter is close to the ship. The proposed control schemes are proved to be robust to the tracking error of its internal loop and results in local exponential stability. The performance of the control system is demonstrated by computer simulations. Currently, work is in progress to implement the algorithm using an instrumented model of a helicopter with a tether.
TwiSty: A Multilingual Twitter Stylometry Corpus for Gender and Personality Profiling

Personality profiling is the task of detecting personality traits of authors based on writing style. Several personality typologies exist, however, the Myers-Briggs Type Indicator (MBTI) is particularly popular in the non-scientific community, and many people use it to analyse their own personality and talk about the results online. Therefore, large amounts of self-assessed data on MBTI are readily available on social-media platforms such as Twitter. We present a novel corpus of tweets annotated with the MBTI personality type and gender of their author for six Western European languages (Dutch, German, French, Italian, Portuguese and Spanish). We outline the corpus creation and annotation, show statistics of the obtained data distributions and present first baselines on Myers-Briggs personality profiling and gender prediction for all six languages.
Medical image analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging.
Characteristics of Generatable Games

We address the problem of generating complete games, rather than content for existing games. In particular, we try to answer the question which types of games it would be realistic or even feasible to generate. To begin to answer the question, we first list the different ways we see that games could be generated, and then try to discuss what characterises games that would be comparatively easy or hard to generate. The discussion is structured according to a subset of the characteristics discussed in the book Characteristics of Games by Elias, Garfield and Gutschera.
Market Index and Stock Price Direction Prediction using Machine Learning Techniques: An empirical study on the KOSPI and HSI

The prediction of a stock market direction may serve as an ear ly recommendation system for short-term investors and as an early financial distress warning system for long-term shareholde rs. In this paper, we propose an empirical study on the Korean and Hong Kong stock market with an integrated machine learning f ramework that employs Principal Component Analysis (PCA) a nd Support Vector Machine (SVM). We try to predict the upward or d wnward direction of stock market index and stock price. In the proposed framework, PCA, as a feature selection method, ide ntifies principal components in the stock market movement an d SVM, as a classifier for future stock market movement, processes t hem along with other economic factors in training and foreca sting. We present the results of an extensive empirical study of the proposed method on the Korean composite stock price index (K OSPI) and Hangseng index (HSI), as well as the individual constitu ents included in the indices. In our experiment, ten years da ta (from January 1st, 2002 to January 1st, 2012) are collected and sch emed by rolling windows to predict one-day-ahead direction s. The experimental results show notably high hit ratios in predic ting the movements of the individual constituents in the KOS PI and HSI. The results also varify the co-movement effect between the Korean (Hong Kong) stock market and the Ameri can stock market.
Memory and Information Processing in Neuromorphic Systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processor architectures is the way in which memory and processing is organized. As information and communication technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper, we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multineuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.
Characterizing Cloud Federation in IoT

The advent of both Cloud computing and Internet of Things (IoT) is changing the way of conceiving information and communication systems. Generally, we talk about IoT Cloud to indicate a new type of distributed system consisting of a set of smart devices interconnected with a remote Cloud infrastructure, platform, or software through the Internet and able to provide IoT as a Service (IoTaaS). In this paper, we discuss the near future evolution of IoT Clouds towards federated ecosystems, where IoT providers cooperate to offer more flexible services. Moreover, we present a general three-layer IoT Cloud Federation architecture, highlighting new business opportunities and challenges.
The ARPA MT Evaluation Methodologies : Evolution , Lessons , and Future Approaches

The ARPA MT Evaluation methodology effort is intended to provide a basis for measuring and thereby facilitating the progress of MT systems of the ARPAsponsored research program. The evaluation methodologies have the further goal of being useful for identifying the context of that progress among developed, production MT systems in use today. Since 1991, the evaluations have evolved as we have discovered more about what properties are valuable to measure, what properties are not, and what elements of the tests/evaluations can be adjusted to enhance significance of the results while still remaining relatively portable. This paper describes this evolutionary process, along with measurements of the most recent MT evaluation (January 1994) and the current evaluation process now underway.
A compact rotary series elastic actuator for knee joint assistive system

Precise and large torque generation, back-drivability, low output impedance, and compactness of hardware are important requirements for human assistive robots. In this paper, a compact rotary series elastic actuator (cRSEA) is designed considering these requirements. To magnify the torque generated by an electric motor in the limited space of the compact device, a worm gear is utilized. However, the actual torque amplification ratio provided by the worm gear is different from the nominal speed reduction ratio due to friction, which makes the controller design challenging. In this paper, the friction effect is considered in the model of cRSEA, and a robust control algorithm is designed to precisely control the torque output in the presence of nonlinearities such as the friction. The mechanical design and dynamic model of the proposed device and the design of a robust control algorithm are discussed, and actuation performance is verified by experiments.
A Minimally Supervised Approach for Synonym Extraction with Word Embeddings

In this paper we present a novel approach to minimally supervised synonym extraction. The approach is based on the word embeddings and aims at presenting a method for synonym extraction that is extensible to various languages. We report experiments with word vectors trained by using both the continuous bag-of-words model (CBoW) and the skip-gram model (SG) investigating the effects of different settings with respect to the contextual window size, the number of dimensions and the type of word vectors. We analyze the word categories that are (cosine) similar in the vector space, showing that cosine similarity on its own is a bad indicator to determine if two words are synonymous. In this context, we propose a new measure, relative cosine similarity, for calculating similarity relative to other cosine-similar words in the corpus. We show that calculating similarity relative to other words boosts the precision of the extraction. We also experiment with combining similarity scores from differently-trained vectors and explore the advantages of using a part-of-speech tagger as a way of introducing some light supervision, thus aiding extraction. We perform both intrinsic and extrinsic evaluation on our final system: intrinsic evaluation is carried out manually by two human evaluators and we use the output of our system in a machine translation task for extrinsic evaluation, showing that the extracted synonyms improve the evaluation metric.
On the role of passion in performance.

The present paper reports two studies designed to test the Dualistic Model of Passion with regard to performance attainment in two fields of expertise. Results from both studies supported the Passion Model. Harmonious passion was shown to be a positive source of activity investment in that it directly predicted deliberate practice (Study 1) and positively predicted mastery goals which in turn positively predicted deliberate practice (Study 2). In turn, deliberate practice had a direct positive impact on performance attainment. Obsessive passion was shown to be a mixed source of activity investment. While it directly predicted deliberate practice (Study 1) and directly predicted mastery goals (which predicted deliberate practice), it also predicted performance-avoidance and performance-approach goals, with the former having a tendency to facilitate performance directly, and the latter to directly negatively impact on performance attainment (Study 2). Finally, harmonious passion was also positively related to subjective well-being (SWB) in both studies, while obsessive passion was either unrelated (Study 1) or negatively related to SWB (Study 2). The conceptual and applied implications of the differential influences of harmonious and obsessive passion in performance are discussed.
Automatic Facial Expression Recognition System Based on Geometric and Appearance Features

This paper presents a computer vision system for automatic facial expression recognition (AFER). The robust AFER system can be applied in many areas such as emotion science, clinical psychology and pain assessment it includes facial feature extraction and pattern recognition phases that discriminates among different facial expressions. In feature extraction phase a combination between holistic and analytic approaches is presented to extract 83 facial expression features. Expression recognition is performed by using radial basis function based artificial neural network to recognize the six basic emotions (anger, fear, disgust, joy, surprise, sadness). The experimental results show that 96% recognition rate can be achieved when applying the proposed system on person-dependent database and 93.5% when applying on person-independent one.
Total Information Quality Management-Capability Maturity Model (TIQM-CMM): An information quality management maturity model

Information quality has received great attention from both academics and practitioners since it plays an important role in decision-making process. The need of high information quality in organization is increase in order to reach business excellent. Total Information Quality Management (TIQM) offers solution to solve information quality problems through a method for building an effective information quality management (IQM) with continuous improvement in whole process. However, TIQM does not have a standard measure in determining the process maturity level. Thus causes TIQM process maturity level cannot be determined exactly so that the assessment and improvement process will be difficult to be done. The contribution of this research is the process maturity indicators and measures based on TIQM process and Capability Maturity Model (CMM) concepts. It have been validated through an Expert Judgment using the Delphi method and implemented through a case study.
A Gate Driver With Integrated Deadtime Controller

Deadtimes are required to avoid simultaneous conduction of high-side and low-side power transistors in half-bridge power converters. During these secure times, free-wheeling current flow generates extra power losses. Very short deadtimes are desired but they cannot be safely set in conventional isolated power converters because of a digital input propagation delay mismatch. A specific deadtime management is introduced in this paper to ensure proper operation of a high-voltage synchronous power converter. A controller integrated in each isolated gate driver secures synchronous switching by detecting the opposite switch turn-off before turn-on. With such a selfswitching technique, very short but safe nonoverlap times can be set. A gate driver has been implemented in a 0.35 μm 20-V CMOS process. The monolithically integrated controller consumes only 140 μA and 0.22 mm2 of silicon area. The proposed local deadtime management has been validated in two synchronous buck converters without external free-wheeling diodes: a 500-W 250-V to 55-V converter based on SiC JFETs and a 30-W 45-V to 10-V converter based on eGaN FETs. In either case, the proposed controller allows a higher efficiency from 10% of the rated load with resulting deadtimes as short as 15 ns.
Judgmental forecasting : A review of progress over the last 25 years

The past 25 years has seen phenomenal growth of interest in judgemental approaches to forecasting and a significant change of attitude on the part of researchers to the role of judgement. While previously judgement was thought to be the enemy of accuracy, today judgement is recognised as an indispensable component of forecasting and much research attention has been directed at understanding and improving its use. Human judgement can be demonstrated to provide a significant benefit to forecasting accuracy but it can also be subject to many biases. Much of the research has been directed at understanding and managing these strengths and weaknesses. An indication of the explosion of research interest in this area can be gauged by the fact that over 200 studies are referenced in this review. D 2006 International Institute of Forecasters.
p88110: A Graphical Simulator for Computer Architecture and Organization Courses

Studying fundamental computer architecture and organization topics requires a significant amount of practical work if students are to acquire a good grasp of the theoretical concepts presented in classroom lectures or textbooks. The use of simulators is commonly adopted in order to reach this objective. However, as most of the available educational simulators focus on specific topics, different laboratory assignments usually require the use of different simulators. This paper presents a graphical and interactive reduced instruction set computer (RISC) processor and memory simulator that allows active learning of some theoretical concepts covered in computer architecture and organization courses. The simulator can be configured to present different processor views, from a simple serial one, without caches or pipelines, to a more realistic one with caches and superscalar execution. This approach allows a set of increasingly complex code-based laboratory assignments to be developed using a single simulator, covering topics ranging from assembly language programming to the analysis of the different kind of cache misses, pipeline hazards or branch prediction hits and misses produced during a program execution. The simulator has been included in a an automatic assessment system that helps the students to complete the assignments and helps teachers to evaluate the correctness of the students' solutions in different environments, such as high-enrollment courses or distance education. Since 1996, both the simulator and the automatic assessment system have been successfully used by more than 5000 students in computer architecture and organization courses at the Technical University of Madrid (UPM), Spain.
Car parking occupancy detection using smart camera networks and Deep Learning

This paper presents an approach for real-time car parking occupancy detection that uses a Convolutional Neural Network (CNN) classifier running on-board of a smart camera with limited resources. Experiments show that our technique is very effective and robust to light condition changes, presence of shadows, and partial occlusions. The detection is reliable, even when tests are performed using images captured from a viewpoint different than the viewpoint used for training. In addition, it also demonstrates its robustness when training and tests are executed on different parking lots. We have tested and compared our solution against state of the art techniques, using a reference benchmark for parking occupancy detection. We have also produced and made publicly available an additional dataset that contains images of the parking lot taken from different viewpoints and in different days with different light conditions. The dataset captures occlusion and shadows that might disturb the classification of the parking spaces status.