nils-herrmann/clean_scirepeval huggingface dataset:
Smooth immersion: the benefits of making the transition to virtual environments a continuous process

In this paper we discuss the benefits and the limitations, as well as different implementation options for smooth immersion into a HMD-based IVE. We evaluated our concept in a preliminary user study, in which we have tested users' awareness, reality judgment and experience in the IVE, when using different transition techniques to enter it. Our results show that a smooth transition to the IVE improves the awareness of the user and may increase the perceived interactivity of the system.
Accurate Heart Rate Monitoring During Physical Exercises Using PPG

The challenging task of heart rate (HR) estimation from the photoplethysmographic (PPG) signal, during intensive physical exercises, is tackled in this paper.  The study presents a detailed analysis of a novel algorithm (WFPV) that exploits a Wiener filter to attenuate the motion artifacts, a phase vocoder to refine the HR estimate and user-adaptive post-processing to track the subject physiology. Additionally, an offline version of the HR estimation algorithm that uses Viterbi decoding is designed for scenarios that do not require online HR monitoring (WFPV+VD). The performance of the HR estimation systems is rigorously compared with existing algorithms on the publically available database of 23 PPG recordings.  On the whole dataset of 23 PPG recordings, the algorithms result in average absolute errors of 1.97 and 1.37 BPM in the online and offline modes, respectively. On the test dataset of 10 PPG recordings which were most corrupted with motion artifacts, WFPV has an error of 2.95 BPM on its own and 2.32 BPM in an ensemble with two existing algorithms.   The error rate is significantly reduced when compared with the state-of-the art PPG-based HR estimation methods.  The proposed system is shown to be accurate in the presence of strong motion artifacts and in contrast to existing alternatives has very few free parameters to tune. The algorithm has a low computational cost and can be used for fitness tracking and health monitoring in wearable devices. The MATLAB implementation of the algorithm is provided online.
Spatial Orientation in Virtual Environments: Background Considerations and Experiments

Spatial orientation strongly relies on visual and whole-body information available while moving through space. As virtual environments allow to isolate the contribution of visual information from the contribution of whole-body information, they are an attractive methodological means to investigate the role of visual information for spatial orientation. Using an elementary spatial orientation task (triangle completion) in a simple virtual environment we studied the effect of amount of simultaneously available visual information (geometric field of view) and triangle layout on the integration and uptake of directional (turn) and distance information under visual simulation conditions. While the amount of simultaneously available visual information had no effect on homing errors, triangle layout substantially affected homing errors. Further analysis of the observed homing errors by means of an Encoding Error Model revealed that subjects navigating under visual simulation conditions had problems in accurately taking up and representing directional (turn) information, an effect which was not observed in experiments reported in the literature from similar whole-body conditions. Implications and prospects for investigating spatial orientation by means of virtual environments are discussed considering the present experiments as well as other work on spatial cognition using virtual environments.
Analysis , Design , and Performance Evaluation of Flying-Capacitor Passive Lossless Snubber Applied to PFC Boost Converter

A boost converter which employs a flying-capacitor passive lossless snubber to reduce the losses caused by the reverse-recovery characteristic of the boost rectifier is described. The passive snubber consists of a snubber inductor, two snubber rectifiers, and a snubber capacitor. The losses are reduced by inserting a snubber inductor in the series path of the boost switch and the rectifier to control the di/dt rate of the rectifier during it’s turn-off. The snubber is analyzed and design guidelines are offered to achieve optimum performance. The proposed snubber is applied to a 500-W power factor corrected (PFC) boost converter which is designed to operate in the universal line range (90-264 VRMS). Performance evaluations of the proposed snubber are made and compared to the conventional boost converter with respect to efficiency and device temperature.
A novel verification method for payment card systems

Security plays a crucial role in payment systems; however, some implementations of payment card security rely on weak cardholder verification methods, such as card and a signature, or use the card without having any cardholder verification process at all. Other vulnerable implementations of cardholder verification methods suffer from many security attacks, such as relay attacks and cloning attacks. In addition, the impact of these security attacks is high since they cause monetary losses for banks and consumers. In this paper, we introduce a new cardholder verification method using a multi-possession factor authentication with a distance bounding technique. It adds an extra level of security to the verification process and utilizes the idea of distance bounding which prevents many different security attacks. The proposed method gives the user the flexibility to add one or more extra devices and select the appropriate security level. This paper argues that the proposed method mitigates or removes many popular security attacks that are claimed to be effective in current card based payment systems, and that it can help to reduce fraud on payment cards. Furthermore, the proposed method provides an alternative verification technique and enables cardholders with special needs to use the payment cards and make the payment system more accessible.
Do neural nets learn statistical laws behind natural language?

The performance of deep learning in natural language processing has been spectacular, but the reasons for this success remain unclear because of the inherent complexity of deep learning. This paper provides empirical evidence of its effectiveness and of a limitation of neural networks for language engineering. Precisely, we demonstrate that a neural language model based on long short-term memory (LSTM) effectively reproduces Zipf's law and Heaps' law, two representative statistical properties underlying natural language. We discuss the quality of reproducibility and the emergence of Zipf's law and Heaps' law as training progresses. We also point out that the neural language model has a limitation in reproducing long-range correlation, another statistical property of natural language. This understanding could provide a direction for improving the architectures of neural networks.
The CLSA Model: A Novel Framework for Concept-Level Sentiment Analysis

Hitherto, sentiment analysis has been mainly based on algorithms relying on the textual representation of online reviews and microblogging posts. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling, and counting their words. But when it comes to interpreting sentences and extracting opinionated information, their capabilities are known to be very limited. Current approaches to sentiment analysis are mainly based on supervised techniques relying on manually labeled samples, such as movie or product reviews, where the overall positive or negative attitude was explicitly indicated. However, opinions do not occur only at document-level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a review. In order to overcome this and many other issues related to sentiment analysis, we propose a novel framework, termed concept-level sentiment analysis (CLSA) model, which takes into account all the natural-language-processing tasks necessary for extracting opinionated information from text, namely: microtext analysis, semantic parsing, subjectivity detection, anaphora resolution, sarcasm detection, topic spotting, aspect extraction, and polarity detection.
Performance and QoE assessment of HTC Vive and Oculus Rift for pick-and-place tasks in VR

In this short paper we propose several objective and subjective metrics and present a comparison between two “commodity” VR systems: HTC Vive and Oculus Rift. Objective assessment focuses on frame rate, impact of ambiance light, and impact of sensors' line of sight obstruction. Subjective study aims at evaluating and comparing the pick-and-place task performance in a virtual world. We collected user ratings of overall quality, perceived ease of use, and perceived intuitiveness, with results indicating that HTC Vive slightly outperforms the Oculus Rift for the pick-and-place task under test.
Gan-Based Domain Adaptation for Object Classification

Recent trends in image classification focus on training deep neural networks that require having a large amount of training images related to the considered task. However, obtaining enough labeled image samples is often time-consuming and expensive. An alternative solution proposed is to transfer the knowledge learned while solving one problem to another but related problem, also called transfer learning. Domain adaptation is a type of transfer learning that deals with learning a model that performs well on two datasets that have different (but somehow correlated) data distributions. In this work, we present a new domain adaptation method based on generative adversarial networks (GANs) in the context of aerial image classification. Experimental results obtained on two datasets for a single object scenario show that the proposed method is particularly promising.
Analysis of the Cost Effectiveness of a Suicide Barrier on the Golden Gate
Bridge

The Golden Gate Bridge (GGB) is a well-known "suicide magnet" and the site of approximately 30 suicides per year. Recently, a suicide barrier was approved to prevent further suicides.



To estimate the cost-effectiveness of the proposed suicide barrier, we compared the proposed costs of the barrier over a 20-year period ($51.6 million) to estimated reductions in mortality.



We reviewed San Francisco and Golden Gate Bridge suicides over a 70-year period (1936-2006). We assumed that all suicides prevented by the barrier would attempt suicide with alternative methods and estimated the mortality reduction based on the difference in lethality between GGB jumps and other suicide methods. Cost/benefit analyses utilized estimates of value of statistical life (VSL) used in highway projects.



GGB suicides occur at a rate of approximately 30 per year, with a lethality of 98%. Jumping from other structures has an average lethality of 47%. Assuming that unsuccessful suicides eventually committed suicide at previously reported (12-13%) rates, approximately 286 lives would be saved over a 20-year period at an average cost/life of approximately $180,419 i.e., roughly 6% of US Department of Transportation minimal VSL estimate ($3.2 million).



Cost-benefit analysis suggests that a suicide barrier on the GGB would result in a highly cost-effective reduction in suicide mortality in the San Francisco Bay Area.
Pneumatic Energy Sources for Autonomous and Wearable Soft Robotics

Many pneumatic energy sources are available for use in autonomous and wearable soft robotics, but it is often not obvious which options are most desirable or even how to compare them. To address this, we compare pneumatic energy sources and review their relative merits. We evaluate commercially available battery-based microcompressors (singly, in parallel, and in series) and cylinders of high-pressure fluid (air and carbon dioxide). We identify energy density (joules/gram) and flow capacity (liters/gram) normalized by the mass of the entire fuel system (versus net fuel mass) as key metrics for soft robotic power systems. We also review research projects using combustion (methane and butane) and monopropellant decomposition (hydrogen peroxide), citing theoretical and experimental values. Comparison factors including heat, effective energy density, and working pressure/flow rate are covered. We conclude by comparing the key metrics behind each technology. Battery-powered microcompressors provide relatively high capacity, but maximum pressure and flow rates are low. Cylinders of compressed fluid provide high pressures and flow rates, but their limited capacity leads to short operating times. While methane and butane possess the highest net fuel energy densities, they typically react at speeds and pressures too high for many soft robots and require extensive system-level development. Hydrogen peroxide decomposition requires not only few additional parts (no pump or ignition system) but also considerable system-level development. We anticipate that this study will provide a framework for configuring fuel systems in soft robotics.
3D Scene Mesh from CNN Depth Predictions and Sparse Monocular SLAM

In this paper, we propose a novel framework for integrating geometrical measurements of monocular visual simultaneous localization and mapping (SLAM) and depth prediction using a convolutional neural network (CNN). In our framework, SLAM-measured sparse features and CNN-predicted dense depth maps are fused to obtain a more accurate dense 3D reconstruction including scale. We continuously update an initial 3D mesh by integrating accurately tracked sparse features points. Compared to prior work on integrating SLAM and CNN estimates [26], there are two main differences: Using a 3D mesh representation allows as-rigid-as-possible update transformations. We further propose a system architecture suitable for mobile devices, where feature tracking and CNN-based depth prediction modules are separated, and only the former is run on the device. We evaluate the framework by comparing the 3D reconstruction result with 3D measurements obtained using an RGBD sensor, showing a reduction in the mean residual error of 38% compared to CNN-based depth map prediction alone.
Application of Random-Effects Pattern-Mixture Models for Missing Data in Longitudinal Studies

Random-effects regression models have become increasingly popular for analysis of longitudinal data. A key advantage of the random-effects approach is that it can be applied when subjects are not measured at the same number of timepoints. In this article we describe use of random-effects pattern-mixture models to further handle and describe the influence of missing data in longitudinal studies. For this approach, subjects are first divided into groups depending on their missing-data pattern and then variables based on these groups are used as model covariates. In this way, researchers are able to examine the effect of missing-data patterns on the outcome (or outcomes) of interest. Furthermore, overall estimates can be obtained by averaging over the missing-data patterns. A psychiatric clinical trials data set is used to illustrate the random-effects pattern-mixture approach to longitudinal data analysis with missing data.
Staring link establishment for high-frequency radio

Progress in signal processing continues to enable welcome advances in high-frequency (HF) radio performance and efficiency. The latest data waveforms use channels wider than 3 kHz to boost data throughput and robustness. This has driven the need for a more capable Automatic Link Establishment (ALE) system that links faster and adapts the wideband HF (WBHF) waveform to efficiently use available spectrum. In this paper, we investigate the possibility and advantages of using various non-scanning ALE techniques with the new wideband ALE (WALE) to further improve spectrum awareness and linking speed.
Antitrust and Costless Verification : An Optimistic and a Pessimistic View of the Implications of Blockchain Technology

Blockchain technology allows a network of individuals, institutions or devices to coordinate economic activity on a global scale (‘internet-level consensus’) without assigning the same degree of control to the intermediary operating and facilitating transactions in the marketplace. This allows for the creation of new types of decentralized digital platforms where the benefits of network effects are separated from the traditional costs they entail in terms of market power. We discuss both the opportunities and challenges the technology involves from an antitrust perspective, and in particular how it can be used to facilitate the creation of extremely efficient and competitive digital markets, as well as to facilitate collusion and make antitrust enforcement more difficult. ∗Christian Catalini: MIT Sloan School of Management, MIT Cryptoeconomics Lab and NBER (catalini@mit.edu). Catherine Tucker: MIT Sloan School of Management, MIT Cryptoeconomics Lab and NBER (cetucker@mit.edu).
"Hey Siri, Do You Understand Me?": Virtual Assistants and Dysarthria

Voice-activated devices are becoming common place: people can use their voice to control smartphones, smart vacuum robots, and interact with their smart homes through virtual assistant devices like Amazon Echo or Google Home. The spread of such voice-controlled devices is possible thanks to the increasing capabilities of natural language processing, and generally have a positive impact on the device accessibility, e.g., for people with disabilities. However, a consequence of these devices embracing voice control is that people with dysarthria or other speech impairments may be unable to control their intelligent environments, at least with proficiency. This paper investigates to which extent people with dysarthria can use and be understood by the three most common virtual assistants, namely Siri, Google Assistant, and Amazon Alexa. Starting from the sentences in the TORGO database of dysarthric articulation, the differences between such assistants are investigated and discussed. Preliminary results show that the three virtual assistants have comparable performance, with an accuracy of the recognition in the range of 50-60%.
Extracting and Ranking Product Features in Opinion Documents

An important task of opinion mining is to extract people’s opinions on features of an entity. For example, the sentence, “I love the GPS function of Motorola Droid” expresses a positive opinion on the “GPS function” of the Motorola phone. “GPS function” is the feature. This paper focuses on mining features. Double propagation is a state-of-the-art technique for solving the problem. It works well for medium-size corpora. However, for large and small corpora, it can result in low precision and low recall. To deal with these two problems, two improvements based on part-whole and “no” patterns are introduced to increase the recall. Then feature ranking is applied to the extracted feature candidates to improve the precision of the top-ranked candidates. We rank feature candidates by feature importance which is determined by two factors: feature relevance and feature frequency. The problem is formulated as a bipartite graph and the well-known web page ranking algorithm HITS is used to find important features and rank them high. Experiments on diverse real-life datasets show promising results.
An educational website on interferometry

The present work describes a website designed for remote teaching of optical measurements using lasers. It enables senior undergraduate and postgraduate students to learn theoretical aspects of the subject and also have a means to perform experiments for better understanding of the application at hand. At this stage of web development, optical methods considered are those based on refractive index changes in the material medium. The website is specially designed in order to provide remote access of expensive lasers, cameras, and other laboratory instruments by employing a commercially available web browser. The web suite integrates remote experiments, hands-on experiments and life-like optical images generated by using numerical simulation techniques based on Open Foam software package. The remote experiments are real time experiments running in the physical laboratory but can be accessed remotely from anywhere in the world and at any time. Numerical simulation of problems enhances learning, visualization of problems and interpretation of results. In the present work hand-on experimental results are discussed with respect to simulated results. A reasonable amount of resource material, specifically theoretical background of interferometry is available on the website along with computer programs image processing and analysis of results obtained in an experiment.
Gather customer concerns from online product reviews - A text summarization approach

Product reviews possess critical information regarding customers’ concerns and their experience with the product. Such information is considered essential to firms’ business intelligence which can be utilized for the purpose of conceptual design, personalization, product recommendation, better customer understanding, and finally attract more loyal customers. Previous studies of deriving useful information from customer reviews focused mainly on numerical and categorical data. Textual data have been somewhat ignored although they are deemed valuable. Existing methods of opinion mining in processing customer reviews concentrates on counting positive and negative comments of review writers, which is not enough to cover all important topics and concerns across different review articles. Instead, we propose an automatic summarization approach based on the analysis of review articles’ internal topic structure to assemble customer concerns. Different from the existing summarization approaches centered on sentence ranking and clustering, our approach discovers and extracts salient topics from a set of online reviews and further ranks these topics. The final summary is then generated based on the ranked topics. The experimental study and evaluation show that the proposed approach outperforms the peer approaches, i.e. opinion mining and clustering-summarization, in terms of users’ responsiveness and its ability to discover the most important topics.
HRCT of interstitial lung disease (ILD): The basic ingredients of the alphabet soup

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