nils-herrmann/clean_scirepeval huggingface dataset:
User acceptance of wearable devices: An extended perspective of perceived value

This study develops a research model for analyzing customers’ perceived value of wearable devices. It investigates the impact of each component of perceived benefit and risk on the perceived value of wearable devices, and explores how their attributes affect customers’ perceived benefit. Partial least squares was employed to test the proposed model and corresponding hypotheses on data collected from 375 survey samples (273 potential and 102 actual users). The results show that perceived value is a clear antecedent of adoption intention. Perceived benefit—including perceived usefulness, enjoyment, and social image—seems to have a greater impact on perceived value than perceived risk. Specifically, a significant difference was observed between potential users and actual users. This study discusses a number of implications and contributes useful insights for researchers as well as
Information sharing on social media sites

This study reports on an exploratory survey conducted to investigate the use of social media technologies for sharing information. This paper explores the issue of credibility of the information shared in the context of computer-mediated communication. Four categories of information were explored: sensitive, sensational, political and casual information, across five popular social media technologies: social networking sites, micro-blogging sites, wikis, online forums, and online blogs. One hundred and fourteen active users of social media technologies participated in the study. The exploratory analysis conducted in this study revealed that information producers use different cues to indicate credibility of the information they share on different social media sites. Organizations can leverage findings from this study to improve targeted engagement with their customers. The operationalization of how information credibility is codified by information producers contributes to knowledge in social media research.
100-epoch ImageNet Training with AlexNet in 24 Minutes

Finishing 90-epoch ImageNet-1k training with ResNet-50 on a NVIDIA M40 GPU takes 14 days. This training requires 10 single precision operations in total. On the other hand, the world’s current fastest supercomputer can finish 2× 10 single precision operations per second (Dongarra et al. 2017). If we can make full use of the supercomputer for DNN training, we should be able to finish the 90-epoch ResNet-50 training in five seconds. However, the current bottleneck for fast DNN training is in the algorithm level. Specifically, the current batch size (e.g. 512) is too small to make efficient use of many processors For large-scale DNN training, we focus on using large-batch data-parallelism synchronous SGD without losing accuracy in the fixed epochs. The LARS algorithm (You, Gitman, and Ginsburg 2017) enables us to scale the batch size to extremely large case (e.g. 32K). We finish the 100-epoch ImageNet training with AlexNet in 24 minutes. Same as Facebook’s result (Goyal et al. 2017), we finish the 90-epoch ImageNet training with ResNet-50 in one hour by 512 Intel KNLs.
The Organizational Impact of Enterprise Architecture: A Research Framework

Many organizations have adopted an Enterprise Architecture (EA) approach because of the potential benefits resulting from a more standardized and coordinated approach to systems development and management, and because of the tighter alignment of business and information technology in support of business strategy execution. At the same time, experience shows that having an effective EA practice is easier said than done and the coordination and implementation efforts can be daunting. While nobody disputes the potential benefits of well architected systems, there is no empirical evidence showing whether the organizational benefits of EA outweigh the coordination and management costs associated with the architecting process. Furthermore, most practitioners we have interviewed can provide technical metrics for internal EA efficiency and effectiveness, but none of our participants were able to provide concrete metrics or evidence about the bottom line impact that EA has on the organization as a whole. In this article we raise key issues associated with the evaluation of the organizational impact of EA and propose a framework for empirical research in this area.
Front-End Factor Analysis For Speaker Verification

It is known that the performance of the i-vectors/PLDA based speaker verification systems is affected in the cases of short utterances and limited training data. The performance degradation appears because the shorter the utterance, the less reliable the extracted i-vector is, and because the total variability covariance matrix and the underlying PLDA matrices need a significant amount of data to be robustly estimated. Considering the “MIT Mobile Device Speaker Verification Corpus” (MIT-MDSVC) as a representative dataset for robust speaker verification tasks on limited amount of training data, this paper investigates which configuration and which parameters lead to the best performance of an i-vectors/PLDA based speaker verification. The i-vectors/PLDA based system achieved good performance only when the total variability matrix and the underlying PLDA matrices were trained with data belonging to the enrolled speakers. This way of training means that the system should be fully retrained when new enrolled speakers were added. The performance of the system was more sensitive to the amount of training data of the underlying PLDA matrices than to the amount of training data of the total variability matrix. Overall, the Equal Error Rate performance of the i-vectors/PLDA based system was around 1% below the performance of a GMM-UBM system on the chosen dataset. The paper presents at the end some preliminary experiments in which the utterances comprised in the CSTR VCTK corpus were used besides utterances from MIT-MDSVC for training the total variability covariance matrix and the underlying PLDA matrices.
MUTAN: Multimodal Tucker Fusion for Visual Question Answering

Bilinear models provide an appealing framework for mixing and merging information in Visual Question Answering (VQA) tasks. They help to learn high level associations between question meaning and visual concepts in the image, but they suffer from huge dimensionality issues.,,We introduce MUTAN, a multimodal tensor-based Tucker decomposition to efficiently parametrize bilinear interactions between visual and textual representations. Additionally to the Tucker framework, we design a low-rank matrix-based decomposition to explicitly constrain the interaction rank. With MUTAN, we control the complexity of the merging scheme while keeping nice interpretable fusion relations. We show how the Tucker decomposition framework generalizes some of the latest VQA architectures, providing state-of-the-art results.
Big Data and Privacy: Emerging Issues

The goals of big data and privacy are fundamentally opposed to each other. Big data and knowledge discovery are aimed reducing information asymmetries between organizations and the data sources, whereas privacy is aimed at maintaining information asymmetries of data sources. A number of different definitions of privacy are used to investigate some of the tensions between different characteristics of big data and potential privacy concerns. Specifically, the author examines the consequences of unevenness in big data, digital data going from local controlled settings to uncontrolled global settings, privacy effects of reputation monitoring systems, and inferring knowledge from social media. In addition, the author briefly analyzes two other emerging sources of big data: police cameras and stingray for location information.
A New Planar Artificial Transmission Line and Its Applications to a Miniaturized Butler Matrix

A miniaturized quadrature hybrid coupler, a rat-race coupler, and a 4 times 4 Butler matrix based on a newly proposed planar artificial transmission line are presented in this paper for application in ultra-high-frequency (UHF) radio-frequency identification (RFID) systems. This planar artificial transmission line is composed of microstrip quasi-lumped elements and their discontinuities and is capable of synthesizing microstrip lines with various characteristic impedances and electrical lengths. At the center frequency of the UHF RFID system, the occupied sizes of the proposed quadrature hybrid and rat-race couplers are merely 27% and 9% of those of the conventional designs. The miniaturized couplers demonstrate well-behaved wideband responses with no spurious harmonics up to two octaves. The measured results reveal excellent agreement with the simulations. Additionally, a 4 times 4 Butler matrix, which may occupy a large amount of circuit area in conventional designs, has been successfully miniaturized with the help of the proposed artificial transmission line. The circuit size of the Butler matrix is merely 21% of that of a conventional design. The experimental results show that the proposed Butler matrix features good phase control, nearly equal power splitting, and compact size and is therefore applicable to the reader modules in various RFID systems.
Reviewing CS1 exam question content

Many factors have been cited for poor performance of students in CS1. To investigate how assessment mechanisms may impact student performance, nine experienced CS1 instructors reviewed final examinations from a variety of North American institutions. The majority of the exams reviewed were composed predominantly of high-value, integrative code-writing questions, and the reviewers regularly underestimated the number of CS1 concepts required to answer these questions. An evaluation of the content and cognitive requirements of individual questions suggests that in order to succeed, students must internalize a large amount of CS1 content. This emphasizes the need for focused assessment techniques to provide students with the opportunity to demonstrate their knowledge.
Anterior Cingulate and the Monitoring of Response Conflict: Evidence from an fMRI Study of Overt Verb Generation

Studies of a range of higher cognitive functions consistently activate a region of anterior cingulate cortex (ACC), typically posterior to the genu and superior to the corpus collosum. In particular, this ACC region appears to be active in task situations where there is a need to override a prepotent response tendency, when responding is underdetermined, and when errors are made. We have hypothesized that the function of this ACC region is to monitor for the presence of crosstalk or competition between incompatible responses. In prior work, we provided initial support for this hypothesis, demonstrating ACC activity in the same region both during error trials and during correct trials in task conditions designed to elicit greater response competition. In the present study, we extend our testing of this hypothesis to task situations involving underdetermined responding. Specifically, 14 healthy control subjects performed a verb-generation task during event-related functional magnetic resonance imaging (fMRI), with the on-line acquisition of overt verbal responses. The results demonstrated that the ACC, and only the ACC, was more active in a series of task conditions that elicited competition among alternative responses. These conditions included a greater ACC response to: (1) Nouns categorized as low vs. high constraint (i.e., during a norming study, multiple verbs were produced with equal frequency vs. a single verb that produced much more frequently than any other); (2) the production of verbs that were weak associates, rather than, strong associates of particular nouns; and (3) the production of verbs that were weak associates for nouns categorized as high constraint. We discuss the implication of these results for understanding the role that the ACC plays in human cognition.
Internet of Things and Big Data Analytics for Smart and Connected Communities

This paper promotes the concept of smart and connected communities SCC, which is evolving from the concept of smart cities. SCC are envisioned to address synergistically the needs of remembering the past (preservation and revitalization), the needs of living in the present (livability), and the needs of planning for the future (attainability). Therefore, the vision of SCC is to improve livability, preservation, revitalization, and attainability of a community. The goal of building SCC for a community is to live in the present, plan for the future, and remember the past. We argue that Internet of Things (IoT) has the potential to provide a ubiquitous network of connected devices and smart sensors for SCC, and big data analytics has the potential to enable the move from IoT to real-time control desired for SCC. We highlight mobile crowdsensing and cyber-physical cloud computing as two most important IoT technologies in promoting SCC. As a case study, we present TreSight, which integrates IoT and big data analytics for smart tourism and sustainable cultural heritage in the city of Trento, Italy.
Static balancing of spatial six-degree-of-freedom parallel mechanisms with revolute actuators

lor arics orllel to n. the of of d e es the lABSTRACT The static balancing of spatial six-degree-of-freedom para lel mechanisms or manipulators with revolute actuators is studi in this paper. Two static balancing methods, namely, using cou terweights and using springs, are used. The first method lea to mechanisms with a stationary global center of mass while t second approach leads to mechanisms whose total potential ergy (including the elastic potential energy stored in the spring as well as the gravitational potential energy) is constant. The p sition vector of the global center of mass and the total potenti energy of the manipulator are first expressed as functions of t position and orientation of the platform. Then, conditions fo static balancing are derived from the resulting expressions. F nally, examples are given in order to illustrate the design metho ologies.
Design and Realization of 3 dB hybrid stripline coupler in 0.5 – 18.0 GHz

In this article the design and the construction of an ultrawideband (UWB) 3 dB hybrid coupler are presented. The coupler is realized in broadside stripline technology to cover the operating bandwidth 0.5 - 18 GHz (more than five octaves). Detailed electromagnetic design has been carried to optimize performances according to bandwidth. The comparison between simulations and measurements validated the design approach. The first prototype guaranteed an insertion loss lower than 5 dB and a phase shift equal to 90° +/- 5° in bandwidth
The Role of Text Pre-processing in Sentiment Analysis

It is challenging to understand the latest trends and summarise the state or general opinions about products due to the big diversity and size of social media data, and this creates the need of automated and real time opinion extraction and mining. Mining online opinion is a form of sentiment analysis that is treated as a difficult text classification task. In this paper, we explore the role of text pre-processing in sentiment analysis, and report on experimental results that demonstrate that with appropriate feature selection and representation, sentiment analysis accuracies using support vector machines (SVM) in this area may be significantly improved. The level of accuracy achieved is shown to be comparable to the ones achieved in topic categorisation although sentiment analysis is considered to be a much harder problem in the literature.
myHealthAssistant: a phone-based body sensor network that captures the wearer's exercises throughout the day

This paper presents a novel fitness and preventive health care system with a flexible and easy to deploy platform. By using embedded wearable sensors in combination with a smartphone as an aggregator, both daily activities as well as specific gym exercises and their counts are recognized and logged. The detection is achieved with minimal impact on the system’s resources through the use of customized 3D inertial sensors embedded in fitness accessories with built-in pre-processing of the initial 100Hz data. It provides a flexible re-training of the classifiers on the phone which allows deploying the system swiftly. A set of evaluations shows a classification performance that is comparable to that of state of the art activity recognition, and that the whole setup is suitable for daily usage with minimal impact on the phone’s resources.
Sensorless Control for High-Speed Brushless DC Motor Based on the Line-to-Line Back EMF

A sensorless control method for a high-speed brushless DC motor based on the line-to-line back electromotive force (back EMF) is proposed in this paper. In order to obtain the commutation signals, the line-to-line voltages are obtained by the low-pass filters. However, due to the low-pass filters, wide speed range, and other factors, the actual commutation signals are significantly delayed by more than 90 electrical degrees which limits the acceleration of the motor. A novel sensorless commutation algorithm based on the hysteresis transition between “90-α” and “150-α” is introduced to handle the severe commutation retarding and guarantee the motor works in a large speed range. In order to compensate the remaining existing commutation errors, a novel closed-loop compensation algorithm based on the integration of the virtual neutral voltage is proposed. The integration difference between the adjacent 60 electrical degrees interval before and after the commutation point is utilized as the feedback of the PI regulator to compensate the errors automatically. Several experiment results confirm the feasibility and effectiveness of the proposed method.
User Profiling in Personalization Applications Through Rule Discovery and Validation

Gediminas Adomavicius New York University adomavic@cs.nyu.edu In many applications, ranging from recommender systems to one-to-one marketing to Web browsing, it is important to build personalized profiles of individual users from their transactional histories. These profiles describe individual behavior of users and can be specified with sets of rules learned from user transactional histories using various data mining techniques. Since many discovered rules can be spurious, irrelevant, or trivial, one of the main problems is how to perform post-analysis of the discovered rules, i.e., how to validate customer profiles by separating “good” rules from the “bad.” This paper presents a method for validating such rules with an explicit participation of a human expert
A 256 kb 65 nm 8T Subthreshold SRAM Employing Sense-Amplifier Redundancy

Aggressively scaling the supply voltage of SRAMs greatly minimizes their active and leakage power, a dominating portion of the total power in modern ICs. Hence, energy constrained applications, where performance requirements are secondary, benefit significantly from an SRAM that offers read and write functionality at the lowest possible voltage. However, bit-cells and architectures achieving very high density conventionally fail to operate at low voltages. This paper describes a high density SRAM in 65 nm CMOS that uses an 8T bit-cell to achieve a minimum operating voltage of 350 mV. Buffered read is used to ensure read stability, and peripheral control of both the bit-cell supply voltage and the read-buffer's foot voltage enable sub-T4 write and read without degrading the bit-cell's density. The plaguing area-offset tradeoff in modern sense-amplifiers is alleviated using redundancy, which reduces read errors by a factor of five compared to device up-sizing. At its lowest operating voltage, the entire 256 kb SRAM consumes 2.2 muW in leakage power.
A correlative analysis process in a visual analytics environment

Finding patterns and trends in spatial and temporal datasets has been a long studied problem in statistics and different domains of science. This paper presents a visual analytics approach for the interactive exploration and analysis of spatiotemporal correlations among multivariate datasets. Our approach enables users to discover correlations and explore potentially causal or predictive links at different spatiotemporal aggregation levels among the datasets, and allows them to understand the underlying statistical foundations that precede the analysis. Our technique utilizes the Pearson's product-moment correlation coefficient and factors in the lead or lag between different datasets to detect trends and periodic patterns amongst them.
Actuator design for high force proprioceptive control in fast legged locomotion

High speed legged locomotion involves high acceleration and extensive loadings of the leg, which impose critical challenges in actuator design. We introduce actuator dimensional analysis for maximizing torque density and transmission `transparency'. A front leg prototype developed based on insight from the analysis is evaluated for direct proprioceptive force control without force sensors. The vertical stiffness controlled leg was tested on a material testing device to calibrate the mechanical impedance of the leg. By compensating transmission impedance from commanded torque, the leg was able to estimate impact force. For the impact test, the mean absolute error as a ratio of full scale sensor force is 0.041 in the 3406 N/m stiffness experiment and is 0.049 in the 5038 N/m experiment. The results indicate that prescribed force profile control is possible during high speed locomotion.
A Meta-Analytic Study of Social Desirability Distortion in Computer-Administered Questionnaires , Traditional Questionnaires , and Interviews

A meta-analysis of social desirability distortion compared computer questionnaires with traditional paper-and-pencil questionnaires and face-to-face interviews in 61 studies (1967— 1997; 673 effect sizes). Controlling for correlated observations, a near-zero overall effect size was obtained for computer versus paper-and-pencil questionnaires. With moderators, there was less distortion on computerized measures of social desirability responding than on the paper-and-pencil measures, especially when respondents were alone and could backtrack. There was more distortion on the computer on other scales, but distortion was small when respondents were alone, anonymous, and could backtrack. There was less distortion on computerized versions of interviews than on face-to-face interviews. Research is needed on nonlinear patterns of distortion, and on the effects of context and interface on privacy perceptions and on responses to sensitive questions.
Comparison between red, green and blue light reflection photoplethysmography for heart rate monitoring during motion

Reflection photoplethysmography (PPG) using 530 nm (green) wavelength light has the potential to be a superior method for monitoring heart rate (HR) during normal daily life due to its relative freedom from artifacts. However, little is known about the accuracy of pulse rate (PR) measured by 530 nm light PPG during motion. Therefore, we compared the HR measured by electrocadiography (ECG) as a reference with PR measured by 530, 645 (red), and 470 nm (blue) wavelength light PPG during baseline and while performing hand waving in 12 participants. In addition, we examined the change of signal-to-noise ratio (SNR) by motion for each of the three wavelengths used for the PPG. The results showed that the limit of agreement in Bland-Altman plots between the HR measured by ECG and PR measured by 530 nm light PPG (±0.61 bpm) was smaller than that achieved when using 645 and 470 nm light PPG (±3.20 bpm and ±2.23 bpm, respectively). The ΔSNR (the difference between baseline and task values) of 530 and 470nm light PPG was significantly smaller than ΔSNR for red light PPG. In conclusion, 530 nm light PPG could be a more suitable method than 645 and 470nm light PPG for monitoring HR in normal daily life.