Reviewerly/exHarmony huggingface dataset:
Do GPS Capable Fitness Watches Accurately Measure Exercise Distances?

Fitness watches have become popular for assessing activity habits. Manufacturers of GPS capable activity monitors claim their devices provide valid and reliable measures of distances traveled. PURPOSE: To determine if six popular physical activity watches provide accurate measures of GPS recorded distances. METHODS: An Apple Watch (AW), Fitbit Ionic (FI), Fitbit Versa (FV), Garmin Vivosport (GVS), Garmin Vivoactive 3 (GVA), and a Garmin Forerunner 35 (GF) were all tested on indoor and outdoor oval tracks, an outdoor walking trail and a zig-zag pattern course. Twenty trials were performed on each course by the same researcher. An industrial TR 88016 FX Series collapsible measuring wheel established actual distance (200 m). Two watches were worn at the same time on the left arm. Each watch was set to walking and their GPS signal was turned on and connected. Distance measured by the wheel and each watch was recorded. Means, standard deviations, and percent relative error were calculated for the GPS distance of each watch. RESULTS: For the outdoor trail condition, the results showed distances ranging from 180.0 to 204.4 m (FI: 180.2 m, 10.4%, FV: 186.6 m, 7.6%, GVA: 200.7 m, 4.0%, AW: 201.1 m, 4.2%, GVS: 204.4 m, 4.8%, GF: 204.4 m, 4.4%). For the zig-zag course condition, the range was from 184.2 to 204.4 m (GVS: 184.2 m, 4.1%, GF: 189.0 m, 6.0%, FI: 189.8 m, 5.6%, FV: 197.9 m, 4.0%, AW: 197.9 m, 4.0%, GVA: 204.4 m, 4.1%). For the outdoor track, the range was from 179.3 to 204.4 (FV: 179.3 m, 10.8%, AW: 189.8 m, 5.6%, FI: 195.5 m, 4.8%, GVS: 200.3 m, 4.0%, GVA: 202.8 m, 4.0%, GF: 204.4 m, 4.1%). For the indoor track, the range was from 164.2 to 169.0 (FI: 164.2 m, 18.3%, AW: 169.0 m, 16%). All Garmin devices failed to record an indoor measurement. CONCLUSIONS: The least and most accurate measurements for each condition were: trail (FI and GVA), zig-zag (GVS and FV), outdoor track (FV and GVS) and indoor track (FI and AW), respectively. The Garmins were the most accurate for the outdoor track with all exhibiting a 4.0% error. Indoor, all devices were less accurate. Only AW and FI had functioning GPS indoor and showed their most error in this setting. Caution must be used when relying on physical activity monitors if accuracy is desired in measuring distance via GPS.
Modelling the Dynamics and Forcesof Wave Energy Converters using WEC-Sim

The waves traveling on the surface of the world’s oceans carry atremendous amount of energy. The ability to convert this energy forhuman use has the potential to help solves the worlds energy probl ...
Model Distillation for Deep-Learning-Based Gaze Estimation

With the recent advances in deep learning, the gaze estimation models reached new levels, in terms of predictive accuracy, that could not be achieved with older techniques. Nevertheless, deep learn ...
End-to-end PGA estimation for earthquake early warning using transformer networks

&amp;lt;p&amp;gt;The key task of earthquake early warning is to provide timely and accurate estimates of the ground shaking at target sites. Current approaches use either source or propagation based methods. Source based methods calculate fast estimates of the earthquake source parameters and apply ground motion prediction equations to estimate shaking. They suffer from saturation effects for large events, simplified assumptions and the need for a well known hypocentral location, which usually requires arrivals at multiple stations. Propagation based methods estimate levels of shaking from the shaking at neighboring stations and therefore have short warning times and possibly large blind zones. Both methods only use specific features from the waveform. In contrast, we present a multi-station neural network method to estimate horizontal peak ground acceleration (PGA) anywhere in the target region directly from raw accelerometer waveforms in real time.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The three main components of our model are a convolutional neural network (CNN) for extracting features from the single-station three-component accelerograms, a transformer network for combining features from multiple stations and for transferring them to the target site features and a mixture density network to generate probabilistic PGA estimates. By using a transformer network, our model is able to handle a varying set and number of stations as well as target sites. We train our model end-to-end using recorded waveforms and PGAs. We use data augmentation to enable the model to provide estimations at targets without waveform recordings. Starting with the arrival of a P wave at any station of the network, our model issues real-time predictions at each new sample. The predictions are Gaussian mixtures, giving estimates of both expected value and uncertainties. The model can be used to predict PGA at specific target sites, as well as to generate ground motion maps.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We analyze the model on two strong motion data sets from Japan and Italy in terms of standard deviation and lead times. Through the probabilistic predictions we are able to give lead times for different levels of uncertainty and ground shaking. This allows to control the ratio of missed detections to false alerts. Preliminary analysis suggest that for levels between 1%g and 10%g our model achieves multi-second lead times even for the closest stations at a false-positive rate below 25%. For an example event at 50 km depth, lead times at the closest stations with epicentral distances below 20 km are 6 s and 7.5 s. This suggests that our model is able to effectively use the difference between P and S travel time and accurately assess the future level of ground shaking from the first parts of the P wave. It additionally makes effective use of the information contained in the absence of signal at other stations.&amp;lt;/p&amp;gt;
Developing an Impartial Game by Mathematical Approach

Abstract In this study, we developed an impartial game namely Nim by using a mathematical approach. The game consists of multiple ways of movement for player winning the game. A total of five players participating in the game testing. The experiment shows that the proposed impartial game for learning process is easy to use.
ROLE OF ADVANCED WEB BASED CONTENT MANAGEMENT SYSTEM AND ITS SIGNIFICANCE IN LIBRARIES MANAGEMENT SYSTEM

Libraries are vaults of learning and the enormous development in computerized assets has constrained library experts to utilize different data innovation tools to oversee and render management to the clients in Chinese education and research sector. To accomplish more noteworthy proficiency in the quickly evolving economic condition, libraries are progressively searching for new standards to convey management to clients in their work areas to innovate their knowledge. Library gateways assume a significant job by making a web domain where clients can without much of a stretch access data. It offers benefits as well as improving insightful correspondence and research among the supporters of the library. This paper discuses the entry of Advanced Web based Content Management System (AW-CMS) using linked list based iterations for database management has been made here to clarify the different supervisionclients, so it mayimprove the successful data searchers and assess accurate data proficiently and reliability in an effective and efficient manner. It likewise features, how it attempts to meet client desires for data on interest in library management and its experimental validation in accordance with existing software systems.
Real, Augmented, Virtual, and Robotic Animals in Smart and Playable Cities

Animals are part of city life. Many animal species live in the city. On the street, in buildings, in our homes, in vacant lots, parks, cemeteries, and in gardens. In smart cities the life of domestic and working animals will be augmented with digital technology. Urban wildlife will be confronted with digital technology as well. Robotic and virtual animals will also become part of street life. We survey the developments that lead to this future city life in which humans, augmented animals, robotic animals and virtual animals will co-exist.
Tiers of Cataloging: A Trim Model Approach for Metadata Capacity

Conversations about what a unit can or cannot do based on staffing volume can be difficult. When libraries make cuts, each unit needs to do more with less or try to say no. Unit leaders need to be able to explain what they can or cannot do. Leaders need to demonstrate capacity with evidence-based data. This article examines the process of calculating and determining the actual capacity of a cataloging unit at University of Michigan Library, how to express capacity in terms of time, quantity, and costs to non-catalogers, and what to do with capacity once it has been determined.
A Visualization Tool for the CIRMMT Distinguished Lecture Series

The CIRMMT Distinguished Lecture (DL) series consists of over seventy 1-h videos of leading scholars and artists discussing their interdisciplinary work, a unique video encyclopedia of Music, Science and Technology. Though this collection in itself is already an invaluable research and pedagogical tool, searching videos in the collection is not a trivial task given the diversity of fields and topics. To facilitate the navigation in the collection, we developed a visualization tool representing textual metadata of videos in a hierarchical bubble chart. In this paper we describe the distinguished lecture series and discuss the iterative prototyping of the visualization tool, as well as avenues for future developments.
LibGuides: Cataloging Toolbox for International Government Information: European Union

Cataloging, classification and subject vocabularies used in creating metadata description for International Governmental Organizations.
Library Services: On Track for your Masters: Business, Economics & Finance

Provides information about 'On Track for Your Masters', an annual workshop programme for postgraduate taught students.
Feature Learning of Image for Unsupervised Texture Segmentation

Segmentation in specific image class, texture feature extraction plays a vital role. But is time consuming and difficult, to develop novel technique to select features manually. Adapting features automatically for particular class of images, will be helpful and effective. Thus, proposed work contributes in texture segmentation, in absence of large learning datasets for learning features. Cost function is used to develop, learning process for matching segmentation model (Mumford Shah model). The feature learning provides essential piecewise image with constraint feature set of compact jump. It is based on convolution feature learning and segmentation performance respectively. It has been reflected, it is possible to learn features of image patch. The approach is effective and produces better texture segmentation for natural images.
Low-Power Hardware Accelerator for Sparse Matrix Convolution in Deep Neural Network

Deep Neural Networks (DNN) have reached an outstanding accuracy in the past years, often going beyond human abilities. Nowadays, DNNs are widely used in many Artificial Intelligence (AI) applications such as computer vision, natural language processing and autonomous driving. However, these incredible performance come at a high computational cost, requiring complex hardware platforms. Therefore, the need for dedicated hardware accelerators able to drastically speed up the execution by preserving a low-power attitude arise. This paper presents innovative techniques able to tackle matrix sparsity in convolutional DNNs due to non-linear activation functions. Developed architectures allow to skip unnecessary operations, like zero multiplications, without sacrificing accuracy or throughput and improving the energy efficiency. Such improvement could enhance the performance of embedded limited-budget battery applications, where cost-effective hardware, accuracy and duration are critical to expanding the deployment of AI.
Do different geologists see the same fractures? Quantifying subjective bias in fracture data collection.

&amp;lt;p&amp;gt;The characterisation of natural fracture networks using outcrop analogues is important in understanding subsurface fluid flow and rock mass characteristics in fractured lithologies. It is well known from decision sciences that subjective bias can significantly impact the way data is gathered and interpreted, introducing scientific uncertainty.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This study investigates the scale of and nature of subjective bias on fracture data collected by geoscientists using four commonly used approaches (linear scanlines, circular scanlines, topology sampling and window sampling) both in the field and in workshops using field photographs.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We observe considerable variability between each participant&amp;amp;#8217;s interpretation of the same scanline, and this variability is seen regardless of participants&amp;amp;#8217; level of geological experience. Geologists appear to be either focussing on the detail or focussing on gathering larger volumes of data; personal character traits that affect the recorded fracture network attributes. As a result, the fracture statistics that are derived from field data can vary considerably for the same scanline, depending on which geologist collected the data. Additionally, the personal bias of geologists collecting the data affects the scanline size (minimum length of linear scanlines, radius of circular scanlines or area of a window sample) needed to collect a statistically representative amount of data.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Based on our findings and on understanding of bias reduction in decision sciences, we suggest protocols to recognise, understand and limit the effect of subjective bias on fracture data biases during data collection.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Our work shows the capacity for cognitive biases to introduce uncertainty into observation-based data. Fracture statistics derived from field data often inputs into geological models that are used for a range of applications, from understanding fluid flow to characterising rock strength, and so these uncertainties have ramifications for propagation into a range of outcomes. Importantly, our findings that personal bias can affect data collection have implications well beyond the geosciences.&amp;lt;/p&amp;gt;
Negotiating big deals: ACRL/SPARC Forum at the 2020 ALA Midwinter Meeting

At the ACRL/SPARC Forum at the 2020 ALA Midwinter Meeting in Philadelphia, three panelists discussed efforts to negotiate with vendors regarding “Big Deal” journal packages, including strategies and information that make such negotiations more effective for libraries. The three panelists provide their remarks below. Gale Etschmaier recounts negotiations between the Florida State University Libraries and Elsevier that led to the successful cancellation of their Elsevier “Big Deal.” Robin Sinn summarizes open access efforts at the Johns Hopkins University Libraries. And, finally, Jason Priem discusses his company’s product Unsub, a “data dashboard that helps libraries forecast, explore, and optimize their alternatives to the Big Deal, so they can unsubscribe with confidence.”
A Formal Approach for Cautious Reasoning in Answer Set Programming (Extended Abstract)

The issue of describing in a formal way solving algorithms in various fields such as Propositional Satisfiability (SAT), Quantified SAT, Satisfiability Modulo Theories, Answer Set Programming (ASP), and Constraint ASP, has been relatively recently solved employing abstract solvers. In this paper we deal with cautious reasoning tasks in ASP, and design, implement and test novel abstract solutions, borrowed from backbone computation in SAT. By employing abstract solvers, we also formally show that the algorithms for solving cautious reasoning tasks in ASP are strongly related to those for computing backbones of Boolean formulas. Some of the new solutions have been implemented in the ASP solver WASP, and tested.
QUALITY OF EDUCATION AS A CATALYST OF EFFICIENCY OF THE INNOVATION PROGRESS OF SOCIETY

The economic and innovational development depend on the quality of human capital, which is determined by the quality of education. The quality of training of pupils and students depends not only on traditional factors (amounts of funding, composition of groups), but also on the qualification of teachers, the level of introduction of new technologies in the educational process. A significant role in increasing the competitiveness of national education systems is played by the English language, which is an imperative condition for innovative breakthroughs, scientific achievements, mastery of new technologies. There is a redistribution of spheres of influence in the field of secondary and higher education at the global level, where the countries of the East Asian region are at the forefront. Attention to postdoctoral training as a prerequisite for improving the efficiency of the innovation process is significantly increasing. The necessity of finding a balance in the teaching of humanities and natural sciences and engineering disciplines in order to reveal the innovative potential of societies in the conditions of rapid technological changes is substantiated.
Cohering and decohering power of Gaussian channels

In this paper, we generalized the concepts of the cohering power and decohering power to Gaussian channels for continuous-variable systems with respect to coherence measures based on the relative entropy and obtained some basic properties. In addition, the cohering power and decohering power of classical noise channels, thermal noise channels and amplifier channels are calculated.
Automatic Extraction of Fractures in Digital Geological Outcrop of Carbonate Rocks and Its Application

Fracture identification and characterization is an important part of fractured reservoir research. To realize the automatic extraction of digital geological outcrop fractures, a fracture extraction method based on multi-scale Beamlet transform is designed. Firstly, the image is enhanced by the adaptive enhancement algorithm to eliminate the influence of noise and uneven illumination. Then the fracture binary image obtained by the Otsu threshold method is used to extract the fracture by Beamlet transform. Finally, discrete line segment connection algorithm is designed to complete the fracture connection. Experiments show that, compared with Canny edge detection algorithm and Hough transform algorithm, the proposed method has better anti-noise ability, and the extracted fracture linear features are more accurate and complete. By adopting adaptive enhancement processing and discrete line segment connection algorithm, the problems of uneven illumination and disconnection of line segments are reduced, and the advantages of Beamlet algorithm in linear feature extraction are fully utilized. Through the analysis of the application of this method on digital outcrop of the Member-2 of Sinian Dengying Formation in Ebian Xianfeng area, it is shown that this method has a good practical application potential in large-scale fracture extraction and complements the study of microscopic fracture identification. It has a good application prospect for improving the interpretation efficiency of digital geological outcrop fractures and has reference value for other linear feature extraction applications.
Inquiry-Based Learning to Enhance Students’ Ability in Poem Paraphrasing

This research seeks to analyze the influence of inquiry-based learning on students’ ability in poem paraphrasing. The research is conducted on the sixth grade students of elementary schools in Setiabudi South Jakarta from January until March 2019. The researchers used experiment method by using posttest only control group design. All sixth grade students of elementary schools in Setia budi are included as the population. The participants of research are chosen by using multi stage random sampling. Based on the calculation of t-test, tcount value is 5.01 and dk=49, while ttable value at the significance level α = 0.05 and dk=49 is 1.66. Therefore, tcount is more than ttable. (5.01 > 1.66) It means that Ho is rejected. The implication of the research is inquiry-based learning has positive impact to enhance students’ ability in poem paraphrasing. Inquiry-based learning is one of the solutions to the teacher when design teaching program of poem paraphrase.
The Effects of middle-aged Women"s Participation in Public Physical 100 Physical Fitness Class for 8 weeks on Body Composition and Basic Physical Fitness

The purpose of this study was to find out the impact of eight weeks of participation in the National Physical Fitness 100 Physical Fitness Class on the physical composition and basic physical strength of middle-aged women. The subjects of this study were 51 women aged 45 or older in the P city National Health Promotion. The subjects compared body composition and basic physical fitness before and after participating in the physical fitness class, and the results of this study are as follows. Body composition did not show significant differences between periods in all three categories: weight, body mass index and body fat rate (p.05). In case of basic physical fitness, muscle strength (t=-4.435, p.001), muscular endurance (t=-7.643, p.001), cardiopulmonary endurance (t=-7.848, p.001), flexibility (t=-4.780, p.001), agility (t=4.17, p.001), power (t= -4.101, p.001)n all six categories, including 000), In all six areas, there were positive and significant differences in timing. Summarizing the above, it can be concluded that the participation of the 8-week national fitness 100 fitness class can have a positive effect on improving the basic fitness of middle-aged women. It is believed that the program will be more efficient in the future, considering long-term program implementation and individual nutrition when operating physical fitness classes.
Integrating RFID technology to protect privacy in library

To solve the problem of library privacy protection, given the defects in the existing service model algorithms based on standard RFID currentlysuch as low time efficiency and poor reversibility, a library privacy protection algorithm based on the RFID technology service model is proposed in this p aper. The original library privacy protection database is converted into the form of RFID technology service model for transaction reduction according to the library privacy of each transaction vector after the conversion. The matrix column vector is used to conduct the “AND” operation and calculate the support degree of candidate set and obtain the frequent itemset. The experimental results show that compared with the original algorithm, the algorithm proposed in this paper can improve the time efficiency while ensuring the false positive rate, with excellent reversibility, security andstronger practicability.
The Visual Search Strategy of Elite Tennis Player’s Serve Return Task depending on Success and Failure

The purpose of this study was to investigate the elite tennis player’s different visual search strategies depending on the serve return success or failure. To achieve this goal, eye movement tracking device and Vision-in-Action(VIA) system were used for analyzing the elite tennis player’s visual search strategies. The result is as follow; First, the ratio of visual fixation time(%) showed significant difference in the ball, waist and impact areas. When the serve return was succeeded, the visual attention increased around impact area, and decreased around the ball and the waist. Second, the eye movement reaction time have been reduced significantly by an average of 0.02 seconds during the success serve return. Third, the visual fixation time of bound area was significantly longer during the service return of success. For the conclusion, the elite tennis player demonstrated different visual search strategies between the serve return success and failure. Moreover, the effective eye movement of visual context strategy and the active visual search pattern have been confirmed during the success trial of serve return tasks.