nils-herrmann/clean_scirepeval huggingface dataset:
Factors affecting online repurchase intention

The purpose of this paper is to investigate the effects of online shopping experience and habit in relation to adjusted expectations for enhancing online repurchase intention.  The authors employed partial least square (PLS) as a technique used to analyze the measurement and structural models. Data for this research were collected from 240 Taiwanese online shoppers who had experienced online shopping at least four times.  The result of this study indicates that online shopping habit acts as a moderator of both customer satisfaction and adjusted expectations, whereas online shopping experience can be considered a key driver for customer satisfaction. Furthermore, the research findings confirm that customer satisfaction is a vital driver of adjusted expectations and online repurchase intention. Adjusted expectations do mediate the impact of online repurchase intention.  This paper highlights the effect of online shopping experience and online shopping habit on enhancing repurchase intention. The result implies that the acquisition of usage experience and spontaneous purchases not only leads to higher customer satisfaction and customer expectations, but also strengthens online repurchase intention. The use of self-report scales suggests the possibility of a common method bias. Future studies may further test the robustness of this study in the interplay of experience and habit to shed more light on their relative importance in explaining online repurchase intention.  This study extends expectancy-disconfirmation paradigm, especially in the context of online shopping, by emphasizing cognitive, affective, and behavioral change on the attitude-intention behavior of online shoppers.
Caterpillar-based cable climbing robot for inspection of suspension bridge hanger rope

Periodic inspection of a hanger rope is needed for the effective maintenance of suspension bridge. However, it is dangerous for human workers to access the hanger rope and not easy to check the exact state of the hanger rope. In this work we have developed a wheel-based robot that can approach the hanger rope instead of the human worker and carry the inspection device which is able to examine the inside status of the hanger rope. Meanwhile, a wheel-based cable climbing robot may be badly affected by the vibration that is generated while the robot moves on the bumpy surface of the hanger rope. The caterpillar is able to safely drive with the wide contact face on the rough terrain. Accordingly, we developed the caterpillar that can be combined with the developed cable climbing robot. In this paper, the caterpillar is introduced and its performance is compared with the wheel-based cable climbing robot.
MVAPICH2 over OpenStack with SR-IOV: An Efficient Approach to Build HPC Clouds

Cloud Computing with Virtualization offers attractive flexibility and elasticity to deliver resources by providing a platform for consolidating complex IT resources in a scalable manner. However, efficiently running HPC applications on Cloud Computing systems is still full of challenges. One of the biggest hurdles in building efficient HPC clouds is the unsatisfactory performance offered by underlying virtualized environments, more specifically, virtualized I/O devices. Recently, Single Root I/O Virtualization (SR-IOV) technology has been steadily gaining momentum for high-performance interconnects such as InfiniBand and 10GigE. Due to its near native performance for inter-node communication, many cloud systems such as Amazon EC2 have been using SR-IOV in their production environments. Nevertheless, recent studies have shown that the SR-IOV scheme lacks locality aware communication support, which leads to performance overheads for inter-VM communication within the same physical node. In this paper, we propose an efficient approach to build HPC clouds based on MVAPICH2 over Open Stack with SR-IOV. We first propose an extension for Open Stack Nova system to enable the IV Shmem channel in deployed virtual machines. We further present and discuss our high-performance design of virtual machine aware MVAPICH2 library over Open Stack-based HPC Clouds. Our design can fully take advantage of high-performance SR-IOV communication for inter-node communication as well as Inter-VM Shmem (IVShmem) for intra-node communication. A comprehensive performance evaluation with micro-benchmarks and HPC applications has been conducted on an experimental Open Stack-based HPC cloud and Amazon EC2. The evaluation results on the experimental HPC cloud show that our design and extension can deliver near bare-metal performance for implementing SR-IOV-based HPC clouds with virtualization. Further, compared with the performance on EC2, our experimental HPC cloud can exhibit up to 160X, 65X, 12X improvement potential in terms of point-to-point, collective and application for future HPC clouds.
Turn-in Folding of the Cephalic Portion of the Lateral Crus to Support the Alar Rim in Rhinoplasty

The hypoplastic, weak lateral crus of the nose may cause concave alar rim deformity, and in severe cases, even alar rim collapse. These deformities may lead to both aesthetic disfigurement and functional impairment of the nose. The cephalic part of the lateral crus was folded and fixed to reinforce the lateral crus. The study included 17 women and 15 men with a median age of 24 years. The average follow-up period was 12 months. For 23 patients, the described technique was used to treat concave alar rim deformity, whereas for 5 patients, who had thick and sebaceous skin, it was used to prevent weakness of the alar rim. The remaining 4 patients underwent surgery for correction of a collapsed alar valve. Satisfactory results were achieved without any complications. Turn-in folding of the cephalic portion of lateral crus not only functionally supports the lateral crus, but also provides aesthetic improvement of the nasal tip as successfully as cephalic excision of the lateral crura.
20 $\mu$ A to 100 mA DC–DC Converter With 2.8-4.2 V Battery Supply for Portable Applications in 45 nm CMOS

A DC-DC buck converter capable of handling loads from 20 μA to 100 mA and operating off a 2.8-4.2 V battery is implemented in a 45 nm CMOS process. In order to handle high battery voltages in this deeply scaled technology, multiple transistors are stacked in the power train.  DC-DC converters are used for internal rail generation for stacking and supplies for control circuits. An I-C DAC pulse width modulator with sleep mode control is proposed which is both area and power-efficient as compared with previously published pulse width modulator schemes. Both pulse frequency modulation (PFM) and pulse width modulation (PWM) modes of control are employed for the wide load range. The converter achieves a peak efficiency of 75% at 20 μA, 87.4% at 12 mA in PFM, and 87.2% at 53 mA in PWM.
Neuropsychological, Cognitive, and Theoretical Considerations for Evaluation of Bilingual Individuals

As the number of bilinguals in the USA grows rapidly, it is increasingly important for neuropsychologists to be equipped and trained to address the unique challenges inherent in conducting ethical and competent neuropsychological evaluations with this population. Research on bilingualism has focused on two key cognitive mechanisms that introduce differences between bilinguals and monolinguals: (a) reduced frequency of language-specific use (weaker links), and (b) competition for selection within the language system in bilinguals (interference). Both mechanisms are needed to explain how bilingualism affects neuropsychological test performance, including the robust bilingual disadvantages found on verbal tasks, and more subtle bilingual advantages on some measures of cognitive control. These empirical results and theoretical claims can be used to derive a theoretically informed method for assessing cognitive status in bilinguals. We present specific considerations for measuring degree of bilingualism for both clients and examiners to aid in determinations of approaches to testing bilinguals, with practical guidelines for incorporating models of bilingualism and recent experimental data into neuropsychological evaluations. This integrated approach promises to provide improved clinical services for bilingual clients, and will also contribute to a program of research that will ultimately reveal the mechanisms underlying language processing and executive functioning in bilinguals and monolinguals alike.
MAPE-K Formal Templates to Rigorously Design Behaviors for Self-Adaptive Systems

Designing software systems that have to deal with dynamic operating conditions, such as changing availability of resources and faults that are difficult to predict, is complex. A promising approach to handle such dynamics is self-adaptation that can be realized by a MAPE-K feedback loop (Monitor-Analyze-Plan-Execute plus Knowledge). To provide evidence that the system goals are satisfied, given the changing conditions, the state of the art advocates the use of formal methods. However, little research has been done on consolidating design knowledge of self-adaptive systems. To support designers, this paper contributes with a set of formally specified MAPE-K templates that encode design expertise for a family of self-adaptive systems. The templates comprise: (1) behavior specification templates for modeling the different components of a MAPE-K feedback loop (based on networks of timed automata), and (2) property specification templates that support verification of the correctness of the adaptation behaviors (based on timed computation tree logic). To demonstrate the reusability of the formal templates, we performed four case studies in which final-year Masters students used the templates to design different self-adaptive systems.
Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration

Data visualization is an essential component of genomic data analysis. However, the size and diversity of the data sets produced by today's sequencing and array-based profiling methods present major challenges to visualization tools. The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution. A key characteristic of IGV is its focus on the integrative nature of genomic studies, with support for both array-based and next-generation sequencing data, and the integration of clinical and phenotypic data. Although IGV is often used to view genomic data from public sources, its primary emphasis is to support researchers who wish to visualize and explore their own data sets or those from colleagues. To that end, IGV supports flexible loading of local and remote data sets, and is optimized to provide high-performance data visualization and exploration on standard desktop systems. IGV is freely available for download from http://www.broadinstitute.org/igv, under a GNU LGPL open-source license.
Bayesian Learning for Safe High-Speed Navigation in Unknown Environments

In this work, we develop a planner for high-speed navigation in unknown environments, for example reaching a goal in an unknown building in minimum time, or flying as fast as possible through a forest. This planning task is challenging because the distribution over possible maps, which is needed to estimate the feasibility and cost of trajectories, is unknown and extremely hard to model for real-world environments. At the same time, the worst-case assumptions that a receding-horizon planner might make about the unknown regions of the map may be overly conservative, and may limit performance. Therefore, robots must make accurate predictions about what will happen beyond the map frontiers to navigate as fast as possible. To reason about uncertainty in the map, we model this problem as a POMDP and discuss why it is so difficult given that we have no accurate probability distribution over real-world environments. We then present a novel method of predicting collision probabilities based on training data, which compensates for the missing environment distribution and provides an approximate solution to the POMDP. Extending our previous work, the principal result of this paper is that by using a Bayesian non-parametric learning algorithm that encodes formal safety constraints as a prior over collision probabilities, our planner seamlessly reverts to safe behavior when it encounters a novel environment for which it has no relevant training data. This strategy generalizes our method across all environment types, including those for which we have training data as well as those for which we do not. In familiar environment types with dense training data, we show an 80% speed improvement compared to a planner that is constrained to guarantee safety. In experiments, our planner has reached over 8 m/s in unknown cluttered indoor spaces. Video of our experimental demonstration is available at http://groups.csail.mit.edu/ rrg/bayesian_learning_high_speed_nav.
Validation of a New General Self-Efficacy Scale

Researchers have suggested that general self-efficacy (GSE) can substantially contribute to organizational theory, research, and practice. Unfortunately, the limited construct validity work conducted on commonly used GSE measures has highlighted such potential problems as low content validity and multidimension-ality. The authors developed a new GSE (NGSE) scale and compared its psycho-metric properties and validity to that of the Sherer et al. General Self-Efficacy Scale (SGSE). Studies in two countries found that the NGSE scale has higher construct validity than the SGSE scale. Although shorter than the SGSE scale, the NGSE scale demonstrated high reliability, predicted specific self-efficacy (SSE) for a variety of tasks in various contexts, and moderated the influence of previous performance on subsequent SSE formation. Implications, limitations, and directions for future organizational research are discussed. Self-efficacy, defined as " beliefs in one's capabilities to mobilize the motivation, cog-nitive resources, and courses of action needed to meet given situational demands ", self-efficacy beliefs vary on three dimensions: (a) level or magnitude (particular level of task difficulty), (b) strength (certainty of successfully performing a particular level of task difficulty), and Authors' Note: We thank Jon-Andrew Whiteman for assistance in data collection and James Maddux, (c) generality (the extent to which magnitude and strength beliefs generalize across tasks and situations). Bandura's restrictive words " given situational demands " have given self-efficacy a narrow focus, and most researchers have limited their research to the magnitude and strength dimensions, conceptualizing and studying self-efficacy as a task-specific or state-like construct (SSE) (e. More recently, researchers have become interested in the more trait-like generality dimension of self-efficacy, which has been termed general self-efficacy (GSE) (e. GSE is defined as " one's belief in one's overall competence to effect requisite performances across a wide variety of achievement situations " (Eden, in press) or as " individuals' perception of their ability to perform across a variety of different situations " (Judge, Erez, et al., 1998, p. 170). Thus, GSE captures differences among individuals in their tendency to view themselves as capable of meeting task demands in a broad array of contexts. have suggested that SSE is a motivational state and GSE is a motivational trait. According to Eden, both GSE and SSE denote beliefs about one's ability to achieve desired outcomes, but the constructs differ in the scope (i.e., generality or specificity) of the performance domain contemplated. As such, GSE and SSE share similar antecedents (e.g., actual …
Chapter 1 NONLINEAR DYNAMICS TIME SERIES ANALYSIS

Much of what is known about physiological systems has been learned using linear system theory. However, many biomedical signals are apparently random or aperiodic in time. Traditionally, the randomness in biological signals has been ascribed to noise or interactions between very large numbers of constituent components. One of the most important mathematical discoveries of the past few decades is that random behavior can arise in deterministic nonlinear systems with just a few degrees of freedom. This discovery gives new hope to providing simple mathematical models for analyzing, and ultimately controlling, physiological systems. The purpose of this chapter is to provide a brief pedagogic survey of the main techniques used in nonlinear time series analysis and to provide a MATLAB tool box for their implementation. Mathematical reviews of techniques in nonlinear modeling and forecasting can be found in Refs. 1-5. Biomedical signals that have been analyzed using these techniques include heart rate [6-8], nerve activity [9], renal flow [10], arterial pressure [11], electroencephalogram [12], and respiratory waveforms [13]. Section 2 provides a brief overview of dynamical systems theory including phase space portraits, Poincare surfaces of section, attractors, chaos, Lyapunov exponents, and fractal dimensions. The forced Duffing-Van der Pol oscillator (a ubiquitous model in engineering problems) is investigated as an illustrative example. Section 3 outlines the theoretical tools for time series analysis using dynamical systems theory. Reliability checks based on forecasting and surrogate data are also described. The time series methods are illustrated using data from the time evolution of one of the dynamical variables of the forced Duffing-Van der Pol oscillator. Section 4 concludes with a discussion of possible future directions for applications of nonlinear time series analysis in biomedical processes.
Unsupervised Methods for Learning and Using Semantics of Natural Language

Teaching the computer to understand language is the major goal in the field of natural language processing. In this thesis we introduce computational methods that aim to extract language structure— e.g. grammar, semantics or syntax— from text, which provides the computer with information in order to understand language. During the last decades, scientific efforts and the increase of computational resources made it possible to come closer to the goal of understanding language. In order to extract language structure, many approaches train the computer on manually created resources. Most of these so-called supervised methods show high performance when applied to similar textual data. However, they perform inferior when operating on textual data, which are different to the one they are trained on. Whereas training the computer is essential to obtain reasonable structure from natural language, we want to avoid training the computer using manually created resources. In this thesis, we present so-called unsupervisedmethods, which are suited to learn patterns in order to extract structure from textual data directly. These patterns are learned with methods that extract the semantics (meanings) of words and phrases. In comparison to manually built knowledge bases, unsupervised methods are more flexible: they can extract structure from text of different languages or text domains (e.g. finance or medical texts), without requiring manually annotated structure. However, learning structure from text often faces sparsity issues. The reason for these phenomena is that in language many words occur only few times. If a word is seen only few times no precise information can be extracted from the text it occurs. Whereas sparsity issues cannot be solved completely, information about most words can be gained by using large amounts of data. In the first chapter, we briefly describe how computers can learn to understand language. Afterwards, we present the main contributions, list the publications this thesis is based on and give an overview of this thesis. Chapter 2 introduces the terminology used in this thesis and gives a background about natural language processing. Then, we characterize the linguistic theory on how humans understand language. Afterwards, we show how the underlying linguistic intuition can be
Oscillometric Blood Pressure Estimation: Past, Present, and Future

The use of automated blood pressure (BP) monitoring is growing as it does not require much expertise and can be performed by patients several times a day at home. Oscillometry is one of the most common measurement methods used in automated BP monitors. A review of the literature shows that a large variety of oscillometric algorithms have been developed for accurate estimation of BP but these algorithms are scattered in many different publications or patents. Moreover, considering that oscillometric devices dominate the home BP monitoring market, little effort has been made to survey the underlying algorithms that are used to estimate BP. In this review, a comprehensive survey of the existing oscillometric BP estimation algorithms is presented. The survey covers a broad spectrum of algorithms including the conventional maximum amplitude and derivative oscillometry as well as the recently proposed learning algorithms, model-based algorithms, and algorithms that are based on analysis of pulse morphology and pulse transit time. The aim is to classify the diverse underlying algorithms, describe each algorithm briefly, and discuss their advantages and disadvantages. This paper will also review the artifact removal techniques in oscillometry and the current standards for the automated BP monitors.
Cellular architecture and key technologies for 5G wireless communication networks

The fourth generation wireless communication systems have been deployed or are soon to be deployed in many countries. However, with an explosion of wireless mobile devices and services, there are still some challenges that cannot be accommodated even by 4G, such as the spectrum crisis and high energy consumption. Wireless system designers have been facing the continuously increasing demand for high data rates and mobility required by new wireless applications and therefore have started research on fifth generation wireless systems that are expected to be deployed beyond 2020. In this article, we propose a potential cellular architecture that separates indoor and outdoor scenarios, and discuss various promising technologies for 5G wireless communication systems, such as massive MIMO, energy-efficient communications, cognitive radio networks, and visible light communications. Future challenges facing these potential technologies are also discussed.