Reviewerly/exHarmony huggingface dataset:
Full-Field Mode Shape Analysis, Alignment and Averaging Across Measurements

Noncontact methods of experimentally acquiring mode shapes and associated natural frequencies eliminate errors induced by mass-weighting of the structure by sensors. Traditional data acquisition methods require costly and delicate sensors such as accelerometers and strain gauges that are time-consuming to setup on each structure needing to be analyzed. Other non-contact data acquisition tools such as Laser Doppler Vibrometers (LDVs) and Digital Image Correlation (DIC) require expensive equipment and placement of speckle patters or high-contrast markers on the structure. Digital video cameras provide a relatively low-cost and portable method to measure a structure with high spatial resolution without needing to modify the structure. Previous work identified a novel variation on Operational Modal Analysis (OMA) to identify full-field mode shapes from video data. This work develops the algorithm’s robustness, investigating effects of camera motion, structure excitation type, and background intensity gradients. Camera motion and modal over-specification are shown to cause identification of modes that do not correspond to physical deformations of the structure. Previously, video stabilization algorithms have been used to eliminate camera motion from video data. These algorithms eliminate most camera motion, but residual motion remains and is identified in additional, spurious mode shapes. When the camera motion is oscillatory, these shapes can be correlated in the frequency domain to the spectrum seen by an accelerometer placed on the camera itself. Averaging techniques are implemented to improve mode shape quality and identify structural and camera modes from spurious modes identified from modal over-specification. When robustly understood, identification of full-field mode shapes and properties can cheaply and efficiently advance structural health monitoring, model verification and updating, change detection, load identification, and other fields of structural dynamics.
A group from Hackenbush game

This research presents the reader some algebraic operations related to combinatorial games and gives a detailed outlook of a special game called Hackenbush game. We will deduce a group with a special feature with the help of some basic algebraic concepts. A fresh outlook to some combinatorial mathematical algebraic operations and concepts through the evaluation of a deduced group from this game.
On Multi-view Interpretation of Convolutional Neural Networks

In this study we consider multi-view capabilities of convolutional neural networks as one of the best methods of representation learning. Multi-view learning as a machine learning technique deals with the task of learning from multiple distinct views or multiple distinct feature sets. Moreover, multi-view feature learning attempts to abstract and summarize distinct feature sets for further machine learning and pattern recognition tasks. In contrast to traditional multi-view learning methods, convolutional neural networks are able to generate representations from unstructured raw data; these features are very essential for real world applications. It is concluded that CNNs are inherently multi-view representation learning methods able to handle both natural and artificial views.
Exploring Public Engagement with the Social Impact of Algorithms

We discuss Logical Conclusion, an analog interactive installation which presents issues surrounding the social impacts of algorithms used by corporations and governments via logic puzzles with physical elements that visitors manipulate to solve. We present the combination of physicality and participation as promising tools to engage the public with the ways that complex technologies interact with society. We also pose questions regarding how such strategies might be extended by the addition of responsive tangible computing elements.
Analyzing the President Joko Widodo’s International Speech: A Study on Critical Linguistics

This paper studies President Joko Widodo self-image through his International speech. In order to do so, the researcher use critical linguistics approach proposed by Fowler (1979) that focuses on Halliday’s Systemic Functional namely textual and ideational function. The data source are in the form of the text speech that performed by Joko Widodo in the International Speech at the World Economic ASEAN 2018. The researcher downloaded the data from YouTube in the form of video then transcribed it into the written form. The researcher chooses the data, formulate and take a note based on the aim of the study. The researcher uses Norman Fairclough theory to analyze the data. Based on Halliday’s metafunction, the writer only analyzes based on textual and social practice analyze. The results can be emphasizing that using grammar; it can reveal speaker’s purpose and help in describing argumentative phases in the speech text. On this way, Joko Widodo can build self-image by deliver his industrial program and opinion through appropriate word choice by delivering his industrial program and opinion through appropriate word choice.
Image filtering using morphological thickness map

In this paper we propose a new algorithm for image filtering using morphological thickness map. Compared to the other smoothing methods, such as anisotropic diffusion, comparative filters, guided and rolling guidance filters, the benefit of our method is that it natively works with the image structure – thickness map, so it does not depend on the various levels of image noise, lightning conditions and effects. We present the method idea, algorithm itself and various experimental results. The results of the filtering using our algorithm can be widely applied in such image processing tasks as image segmentation, motion analysis, invariant feature transformation, data compression.
Enabling SMEs to Conduct User Research: Experience with and Adaption of UX Concept Exploration

We present an update from 6 years of practical use of UX Concept Exploration, a method for user research and user-driven innovation. After a short overview of the method, the paper describes experiences and suggests modifications of the method, where appropriate. Finally, we discuss guidelines to make the method usable in environments with only little UX expertise and limited resources, like small and medium-sized enterprises (SMEs) usually face.
Robust Health Score Prediction from Pyro-Sensor Activity Data based on Greedy Feature Selection

Automated activity assessment using IoT/smartphone sensors becomes great popular in ubiquitous computing research community recent year thanks to the enhancement of mobility and IoT sensing. In these researches, owing to the great success of statistical machine learning technique called Lasso, the work offers the interpretability of the model. However, in some sparse feature condition, Lasso as a l1 regression method could not give a satisfying result for prediction precision and feature selection. In this paper, we propose a new prediction scheme using greedy feature selection method which is expected to be effective under large scale feature in limited number of dataset. With the help of the new scheme, we could solve the overfitting problem when using l1 regression as well as giving satisfying prediction result. Experimental results using longitudinal pyro-sensor dataset of health score of elderly people show that our new scheme offers better interpretability as well as achieves better prediction accuracy compared with Lasso.
The Nature of Browsing

Journal of the Association for Information Science and TechnologyVolume 71, Issue 5 p. 616-616 LETTER TO THE EDITOR The Nature of Browsing Marcia J. Bates, Corresponding Author Marcia J. Bates mjbates@ucla.edu orcid.org/0000-0003-1761-2217 Department of Information Studies, University of California, Los Angeles (UCLA), Los Angeles, CA, 90095-1520Search for more papers by this author Marcia J. Bates, Corresponding Author Marcia J. Bates mjbates@ucla.edu orcid.org/0000-0003-1761-2217 Department of Information Studies, University of California, Los Angeles (UCLA), Los Angeles, CA, 90095-1520Search for more papers by this author First published: 19 June 2019 https://doi.org/10.1002/asi.24260Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume71, Issue5May 2020Pages 616-616 RelatedInformation
The feed forward neural network with genetic algorithm for daily stock prediction

Abstract The power of Feed Foward Neural Network (FFNN) in conjungtion with Genetic Algorithm (GA) was applied in this research to predict daily stock price. Finance time series data has a high complexity, so that the acurate prediction is hard to be gained by standard model. Machine learning becomes the new prediction tool which is often used because of its adaptive properties. Neural Network (NN) is one of the machine learning which able to complete inference tasks such as prediction, especially in large data sets. FFNN is one of the NN models that has simple network architecture. In the standard optimization method, the initial weights is randomly selected to desire the optimum solution. But It usually raises the problem of unsteady estimation. The GA optimization method was applied in this research to overcome this lack. GA optimizes any function effectively and seeks a global optimum solution efficiently. GA implementation on the FFNN was aimed to obtain optimum weights that minimizing the error. The daily stock price prediction of PT. Adhi Karya Tbk had RMSE of training and testing data at 51.2531 and 44.8706 respectively. This result was equivalent with MAPE values at 1.5714% and 1.5501%.
Impossibility results for belief contraction

Three seemingly weak and plausible conditions on an operation of contraction on belief sets are shown to be logically incompatible: (1) there is at least one sentence that can be successfully removed by the operation, (2) both the original belief set and the outcome of the operation have finite representations, and (3) a non-tautologous sentence can be removed without loss of all its non-tautologous logical consequences.
Bivariate BRDF Estimation Based on Compressed Sensing

We propose a method of estimating a bivariate BRDF from a small number of sampled data using compressed sensing. This method aims to estimate the reflectance of various materials by using the representation space that keeps local information when restored by compressed sensing. We conducted simulated measurements using randomly sampled data and data sampled according to the camera position and orientation, and confirmed that most of the BRDF was successfully restored from 40% sampled data in the case of simulated measurement using a camera and markers.
The Role of Participative and Practical Goal-Setting in MIS Students Learning and Performance

Envisaging massive opportunities student would benefit from an enterprise systems company's university alliance program, we examined the influence of goal-setting, i.e., passing the certification exam, on students learning and performance in class. Also, we explored and elucidated the student cognitive and emotional responses to the collaborative program. Applying a qualitative case study and grounded theory method, we developed a conceptual model of participative and practical goal-setting that could advance current knowledge in enterprise systems education and general goal-setting research.
Quron: Basic Representation and Functionality

The paper presents an approach to represent a quantum bit as a neuron, which uses a threshold function. The functionality of the perceptron model of a neuron is modeled using qubit, which can be used as a building block for the quantum neural network. Several approaches for building and training a Quantum Neural Network have been proposed, namely step function as measurement, quantum dots, quantum associative memory, and perceptron models. But, the functional model of the elementary quantum neuron is not described in much detail. We attempt to build such quantum neuron using qubits and perform its function using qubit rotation operation.
Impact of Mobile Network Operators’ Incentive for Public Health: A Study Based on Saudi 2030 Vision

Obesity is an escalating health problem in many countries. The Kingdom of Saudi Arabia (KSA) ranks third in the world in obesity rates, according to the Saudi Health Ministry’s (SHM) latest reports with up to 60% of the total population classified as obese. This study is consistent with the vision of the SHM 2030 and its 2020 transformational program, which includes the creation of a health system that contributes to the promotion of both individual and societal health through incorporating and activating the role of Information Technology (IT) and digital transformation in its health system. The study adopted a quantitative research approach to obtain the most accurate and generalizable results. Study data were collected from 417 participants from all regions of the KSA. Most participants agreed that obtaining free rewards from Mobile Network Operators (MNO) encourages them to practice healthy walking and to put healthy walking among their health priorities.
How Should "AI Speakers" Touch Consumer Hearts?

This study investigates consumer interest and concerns about Artificial Intelligence (AI) speakers. An AI speaker, otherwise called a smart speaker, is a wireless speaker and voice command device with an integrated interactive virtual assistant. A general understanding of social issues about and interest in AI technology is acquired through an analysis of keywords on mass media in discussions where AI technology is applied in real life.
Using Hardware Software Codesign for Optimised Implementations of High-Speed and Defence in Depth CAESAR Finalists

In this work, we present five optimised implementations on a Xilinx-Zynq7200 SoC for the high-speed and defence in depth finalists of the CAESAR competition for finding authenticated encryption ciphers. We eliminated the standard interfaces used during the competition. Through optimised interfaces between hardware and software, we were able to get both performance improvements as well as reduction in used programmable logic. The performance of our implementations is comparable to pure hardware implementations, but our implementations are 50% smaller. Compared to pure SW implementations we are 16 times faster. Comparing the different algorithms, we come to the conclusion that Colm allows the fastest implementation.
Deep Learning-Based V2V Channel Estimations Using VNETs

The development of cooperative intelligent transportation systems brings new challenges to wireless communication technologies, where the channel estimation becomes more and more important. In this paper, a novel data-driven channel estimation method based on deep learning framework is adopted. Based on the feedforward neural network, the VNET neural network based on the convolutional neural network is proposed. The simulations and practical measurements are also provided to verify the performance advantages. The results show the achieved performance advantages of the proposed VNET-based method, which is shown to be an effective solution.
Understanding and Strengthening Capacity for Society [Last Word]

In this issue, our community addressed advantageous, and disadvantageous, utilization of capacity in society. We assessed constraints that limit capacity. We addressed underutilization of capacity. We explored the potential for adjustments to throughput, with the hopes of improving capacity.
Scale Your Team Horizontally

Not long ago, when your company became successful, you bought a bigger computer to run your software. We called this scaling vertically . Today, that is less common, in part because we have gotten quite good at scaling horizontally so when your company becomes successful, it buys more of the same-sized computers.
Genetic algorithm based sentence packaging in natural language text generation

Abstract Sentence packaging is an important task in natural language text generation which could be treated as a particular kind of a community detection problem. We propose an approach based on genetic algorithm and predictive machine learning models to advance it. The approach allows handling large ontological and semantic structures in a form of a graph to produce well-formed sentences. The results of experiments showed that the genetic algorithm optimizing the modularity measure gives comparable results to ones achieved by a traditional community detection algorithm and outperforms it on a collection of relatively short texts. The design of an approach allows for further introducing linguistic characteristics into a fitness function that gives it a high potential to increase the quality of detected packages while taking into account the specificity of the domain.
Comparative Study of Machine Learning Approaches for Heart Transplantation

Heart failure is a severe medical case, where the heart is not able to function properly to maintain blood flow. The surgical heart transplant procedure accomplished with the last stage of failure of the heart. Machine learning approaches account an ability to handle large datasets systematically and are extensively used in a biomedical research field. With the help of machine learning algorithms, tools are developed that helps specialist as a successful mechanism. The objective of this study is to learn different machine learning approaches for analyzing the heart transplantation dataset by using suitable classification algorithm. Also, the theoretical and the experimental comparative study of different machine learning techniques, using heart transplantation data. This study provides basic guidelines on machine learning technique. The results provide an overview of machine learning technique. We have used a WEKA machine learning software for evaluation and analysis to get an easy way to understand the result.
File Assignment Control for a Web System of Contents Categorization

This paper shows the effect of the controlling file assignment on the file transfer time for a Web-based content categorization system. Our proposed algorithm estimates categories of contents based on the terms and the content categories already added. However, our algorithm uses a large table that consists of the scores that represent the relationship between a term and a category. To address the large table size and longer calculation time, we proposed a distributed Web system that uses multiple calculation machines. This Web system runs preprocessors on a Web browser and calculation machines. In this Web system, the file transfer time becomes a problem when a user sends larger files. In this paper, we propose a way to resolve the issue of longer file transfer time by controlling the file assignment. We assign the large files to the Web browser process, and we assign the smaller files to the calculation machines over the network.
A Dynamic Scene Recognition Method for Event-Based Social Network

With the development of event-based social network, the ways and tools of people’s online and offline social activities have changed dramatically. IEBSN emphasizes the impromptu of social events, which will lead to inaccurate and inefficient event recommendation. To address this problem, we propose a novel dynamic scene recognition method for IEBSN, which combines supervised learning and unsupervised learning. Aiming at the similarity and inconsistency between classes in scene recognition, we propose a convolution feature encoder, which can extract more scene visual information. In order to meet the unexpected requirement of application level, the images whose convolution module is lower than a certain threshold are clustered with k-means. Experiments show that this method can automatically identify the scene in IEBSN application efficiently, and alleviate the problems in scene recognition.
Security analysis of a security-enhanced certificateless signature scheme

To solve the problem of certificate management and key escrow in traditional signature, certificateless signature scheme has attracted wide attention since it was put forward. Recently, Yuan et al. presented a CLS scheme and gave its security proof in the standard model. It proved that the scheme satisfies the existential unforgeability and its security relies on the CDH assumption. However, we find that the security proof of Yuan et al.'s scheme is seriously flawed. In this paper, we present an attack against Yuan et al.'s scheme. The final security analysis results show that there is a non-negligible probability to distinguish the probability distribution of the simulated signature and the real signature. It means that it can be distinguished between the simulated game and the real game. More importantly, the security of Yuan et al.'s scheme can not be reduced to the CDH assumption.
Double Reliability Evaluation of Intelligent Diagnostic Methods baesd on DGA

The classical DGA fault diagnosis methods, their diagnostic characteristics information from different angles, such as gas concentration, gas ratio, gas ratio, and the actual transformer faults is relatively complex. Due to various methods to diagnosis characteristics and criterion, the difference of the result also often appear inconsistent when using these methods in the diagnosis of the fault conclusion, their reliability in diagnosis of different fault types are not the same. Therefore, accurately assess the reliability of the diagnostic method difference, for the selection of diagnosis methods in practical, reasonable use of the characteristics of diagnosis and new diagnostic criterion of the proposed has the vital role.
Visualization of the Cosmic Rays Using 3D and Augmented Reality Techniques

The cosmic rays are formed mainly by protons and atomic nuclei that originate from extraterrestrial sources and continuously reach Earth's atmosphere. When they reach the atmosphere, they are fragmented into smaller pieces producing cascades of secondary particles. The analysis of secondary particles that reach the surface, by specific detectors, can provide very valuable information about different events such as; the activity of the sun, changes in the geomagnetic field or data on the atmosphere. The measures developed are influenced by the environment in which the detector is installed, since various elements of the building or environment in which it is located can interact with the rays. In this document, we present the novel application to the visualization of the cosmic rays measured in a high-resolution detector by 2D, 3D and augmented reality techniques. The detector used is located at the Faculty of Physics of the University of Santiago de Compostela, Spain and is known as TRAGALDABAS.
An evaluation of the interactive video CAPTCHA method against automated attack

CAPTCHA is designed to detect automated programs (called bots) by requiring them to perform tasks that are easy for humans but difficult for automations. CAPTCHAs are vulnerable to relay attacks in which the challenges are relayed to remote human-solvers. In our previous paper, we proposed an interactive video type CAPTCHA that is strongly resistant to relay attacks. However, a quantitative evaluation of resistance to automated attacks still has not been carried out. Herein, we implement an automated attack for applying to our CAPTCHA and evaluate its resistance to automated attacks. Our results show the robustness of our proposed method against mean shift algorithm.