Reviewerly/exHarmony huggingface dataset:
Research on the Positioning Technology of Sports 3D Teaching Action Based on Machine Vision

Abstract This paper presents a method of action location in three-dimensional motion teaching. The machine vision technology is used to solve the problems of low positioning accuracy and long positioning time in the traditional motion three-dimensional teaching method. The work of this method is as follows: (a) using machine vision method to determine the world coordinate system of the image; (b) using MRF algorithm to extract the features of 3D teaching action image; (c) determining the spatial correlation of 3D teaching action data. In the three-dimensional teaching action image, the smooth filtering technology is used to suppress and eliminate the noise. Then the convolution neural network (CNN) is used to reconstruct the three-dimensional teaching action image. The entropy of three-dimensional teaching behavior of physical education is determined by CNN. Through a large number of computer simulations, the effectiveness of the proposed system is confirmed. The results show that the system achieves 95% accuracy when the positioning time is 1.9 s.
Pilot Study for Two Questionnaires Assessing Intentions of Use and Quality of Service of Robots in the Hotel Industry

This paper is a pilot study for a questionnaire assessing intentions and expectations of use of robots in the hotel industry, as well as for an adaptation of the quality of service questionnaire Servqual adapted for use with robots in the hotel industry. Toward that aim, a sample of 157 hotel staff and owners in Greece completed the new 28-item intentions questionnaire and the adapted 22-item Servqual questionnaire. The factorial structure of the questionnaire was examined with the use of principal components analysis with direct obliging rotation. Intention and expectations of the use of robots in the hotel industry (Intention-Expectations of Robot Use) were comprised of six factors (convenience and advantages of use of robots in the hotel industry; staff familiarization with new technologies and auxiliary work of robots; anthropomorphic characteristics of robots; absence of concern for human position loss or role change; high cost of buying and using robots; financial improvement in hotels and robot discretion), which showed high and acceptable reliability. Quality of service of robots in the hotel industry (Servqual of Robots) was comprised of three factors (tangibles and reliability; individualized fulfillment of needs; exceptional service), which deviated from the five-factor solution for Servqual (Parasuraman et al., J Retailing 67:420–450, 1991), a finding that may be attributable to the fact that a differential factor structure may be needed to explain quality of service in the novel application of robots in the hotel industry. The three factors of Servqual also showed high reliability. Face validity and content validity were satisfactory for both scales, and there was statistically significant evidence for the concurrent validity of the two scales.
FPGA Implementation of an ECC Processor Using Edwards Curves and DFT Modular Multiplication

In this work, an elliptic curve cryptography (ECC) processor is proposed. The ECC processor has been designed based on Edwards curves defined over the finite prime field GF ((2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">13</sup> - 1) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">13</sup> ). Modular multiplication in the proposed ECC processor is carried out in the frequency domain using a Discrete Fourier Transform (DFT) modular multiplier. Different base field adders and base field multipliers have been designed and utilized in the design of the DFT modular multiplier. The ECC processor has been described and functionally tested using the VHDL language and the simulation tool in the Xilinx ISE14.2. Furthermore, the ECC processor has been synthesized using the synthesis tool in the Xilinx ISE14.2, targeting the Virtex-5 FPGA family. Our synthesis results show that the proposed ECC processor achieves higher speed with minor area penalty compared to the similar work in the literature.
An Actor-Critic Ensemble Aggregation Model for Time-Series Forecasting

Ensemble models are widely used as an effective technique in time-series forecasting, and recently, are inclined toward leveraging meta-learning methods due to their proven predictive advantages in combining individual models in an ensemble. However, finding the optimal strategy for ensemble aggregation is an open research question, particularly, when the ensemble needs to be adapted in real-time. In this paper, we pro-pose a novel meta-learning approach for aggregation of linearly weighted ensembles for the task of time-series forecasting. We outline a deep reinforcement learning framework with a coherent design of the components of the environment and the objective function as an aggregation method in our task. In this framework, the combination policy in ensembles is modeled as a sequential decision making process which is able to capture the temporal behavior in time-series, and an actor-critic model aims at learning the optimal weights in a continuous action space. An extensive empirical study on various real-world datasets demonstrates that our method achieves excellent or on par results in comparison to the state-of-the-art approaches as well as several baselines.
A mixed-methods approach to understanding laptop-free zones in college classrooms

Laptop use within college courses has been a topic of debate for nearly two decades. Completely banning laptops from college classes may seem appealing given the potential drawbacks, but these policies may have unintended consequences. One seemingly successful intervention that can address these conflicting goals is the use of laptop zones and laptop-free zones which allows students who want to use their laptops to continue to do so, without hindering the performance of those who do not. We examine the efficacy of this intervention through the lens of the theory of self-regulated learning. We implemented laptop(-free) zones in two large college courses with a total of 168 students, 60 of whom reported sitting in a laptop-zone. Students sitting in the laptop-free zone performed better on objective measures of learning on the day of learning new material, but this difference was not significant five days later on a measure of retention. Further, there were no differences between these groups on self-rated performance at the point of retention. By examining open-ended responses regarding their motivation to sit in the laptop-free zone or the laptop-zone, we found students who sat in the laptop-free zone typically reported wanting to avoid distractions and enhance their memory of the material/class performance compared to those who sat in the laptop zone. Also, more individuals who sat in the laptop zone reported doing so to ensure completeness of their notes, to look up extra course-related material, and to complete non-course related tasks while listening to the lecture. Lastly, we pose a set of future directions aimed at learning more about the use of laptops in and outside of class, and how we can help students be successful using laptops for learning.
Source Process-Related Delays in Earthquake Early Warning for Example Cases in Greece

ABSTRACT We explore a hypothetical zero-latency earthquake early warning (EEW) system in Greece, aiming to provide alerts before warning thresholds of the intensity of ground motion are exceeded. Within the seismotectonic context of Greece, both shallow- and intermediate-depth earthquakes (along the Hellenic subduction zone) are plausible and, thus, examined. Using regionally applicable attenuation relations, we combine and adjust the methodologies of Minson et al. (2018) and Hoshiba (2020) to examine what are the minimum magnitudes required to invoke the warning thresholds at the user site. With simple modeling, we examine how fast an alert can be issued and what is the available warning time when taking into account delays due to finite-fault rupture propagation, alongside other delays. These computations are merged with delays introduced due to the present-day configuration of the Greek national monitoring network (varying spatial density of permanent monitoring stations). This approach serves as a tool to assess the feasibility of an EEW system at specific sites and to redesign the national permanent monitoring network to serve such a system more effectively (we provide results for four sites.). Warning times for on-land crustal earthquakes are found to be shorter, whereas for intermediate-depth earthquakes in Greece an EEW system is feasible (provides warning times of several tens of seconds at large cities, e.g., on Crete Island) even with the current configuration of the national monitoring network, which is quite sparse in the southern part of the country. The current network configuration also provides sufficient early warning (e.g., of the order of 10 s for a warning threshold of 0.05g) at the center of Athens from earthquakes of the eastern Gulf of Corinth—a zone posing elevated hazard in the broader area of the Greek capital. Several additional assumptions and factors affecting the operability of an EEW system in Greece (i.e., source process complexity and uncertainty in attenuation laws) are also discussed.
User Distributions in Shard-based Blockchain Network

Sharding is one of the most promising and practical methods to achieve horizontal scalability of blockchain networks. However, the increasing number of cross-shard transactions in blockchain sharding protocols may degrade the system throughput. In this paper, we investigate how to distribute users properly in the shard-based blockchains to boost the system transaction performance. We first build an open Jackson queueing network model to capture users' transaction dynamics on shards. Then we cast users' interactions as a shard-based blockchain game, wherein each user aims to minimize its transaction confirmation time and transaction fee. We investigate the equilibrium of the game, and design a polynomial-time algorithm to find efficient equilibria with good system performance. We further design a novel sharding protocol with dynamic user distribution for the permissionless blockchain, and the protocol can maintain good performance in long-term dynamic environment. Extensive numerical results using realistic blockchain transaction data demonstrate that the proposed algorithm and the designed protocol can achieve superior performance for shard-based blockchains.
Modulation Classification Based on Statistical Features and Artificial Neural Network

Modulation classification has been an interesting topic for many years with many applications in both civil and military fields. In this paper, we propose a feature-based automatic modulation recognition method that is based on the time domain statistical features of the amplitude envelope and the instantaneous phase with an artificial neural network (ANN) classifier. We trained and tested the classifier to classify four different digital modulation schemes: BPSK, QPSK, 16QAM and 64QAM. The classifier performance was compared to one of the feature-based methods in literature and simulation results show that the proposed method performs better especially in lower signal to noise ratios (SNRs). The presented method is also tested with the over-the-air captured signal dataset (RadioML.2018) and gave a maximum of 99% correct classification in high SNRs.
A Simulation of Mobile Transportation Robot for Agricultural Products Handling to Warehouse

This research article aims to present the simulation of mobile transportation robot for agricultural products handling to warehouses. The simulation of mobile transportation robot contains two tests. The first one is the test of detected colors of the work pieces by color sensors. It was found that the color sensors were able to detect the colors of the work pieces. However, the 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> and the 7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> round of the test, gray color was wrongly detected due to the similarity of black and gray. It was also caused by the absorption of high intensity colors. Once detected, there was no reflection the color sensor. The second one is the test of mobile transportation robot that put work pieces in position of warehouse simulated point. The results of the second test revealed that, after 10 rounds per mobile transportation robot, we received four work pieces. The position of the designated warehouse simulated point included jasmine rice (A), durian (B), rubber (C) and palm oil (D). In conclusion, the robot put efficiently and precisely objects in warehouses simulated point.
A Multi-modal Audience Analysis System for Predicting Popularity of Online Videos

Over the last decade, the volume of videos available on the web has increased exponentially. In order to help users cope with the ever-growing video volume, recommendation systems have emerged that can provide personalized suggestions to users based on their past preferences and relevant online metrics. However, such approaches require user profiling, which raises privacy issues while often providing delayed suggestions as various metrics have to be firstly collected such as ratings and number of views. In this paper, we propose a system specifically targeting video content generated in a conference event, where a series of talks and presentations are held and a separate video for each is recorded. Through audience analysis, our system is able to predict the online views of each video and thus recommend the most popular videos to users. This way, online users don’t have to search through all the videos of a conference event thus saving time while not missing the most impactful videos. The proposed system employs several complementary techniques for audience analysis based on video and audio streams. Experimental evaluation of real data demonstrates the potential of the proposed approach.
A mining pool solution for novel proof-of-neural-architecture consensus

In many recent novel blockchain consensuses, deep learning training procedure becomes the task for miners to prove their workload, thus the computation power of miners will not purely be spent on the hash puzzle. Therefore, the hardware and energy will support the blockchain service and deep learning training at the same time. The incentive of miners is to earn tokens and individual miners will find mining pools become more competitive. To the best of our knowledgeWe are the first to demonstrate a mining pool solution for novel consensuses based on deep learning. This work adopts from exist Proof-of-Deep-Learning (PoDL) as the consensus and Neural Architecture Search (NAS) as the workload. The mining pool manager partitions the full searching space into subspaces and all miners contributes to the NAS task in the assigned tasks. The strong miners are assigned for exploration and the weak miners are assigned for exploitation. In section IV, it shows the performance of this mining pool is more competitive than an individual miner in conducting NAS as workload.
Observations on Performing Sine Waves with an Oscillator Ensemble

Abstract Founded in 2016 at Université de Montréal, Ensemble d’oscillateurs (French for “Oscillator Ensemble”) brings together 10 musicians around old analog test equipment oscillators that produce audio sine waves. The ensemble performs new compositions while also arranging early electronic pieces. In parallel, the project has developed itself as a space to gather information and reflect on sine wave–based music. In this article, the author presents some of the key considerations and challenges in the formation of Ensemble d’oscillateurs. Based on observations made throughout the development of a body of work using these audio oscillators, he then aims to open a discussion on some aspects of the historical trajectory of the use of sine waves in modern music.
An Analytical Review of Data Access Control Schemes in Mobile Cloud Computing

Owing to the cryptographic-based access control property, ciphertext policy attribute-based encryption (CP-ABE) has been widely adopted as an effective solution for data access control in cloud computing. Nevertheless, CP-ABE cryptographic construct is based on bilinear paring rendering high computation cost. Therefore, using CP-ABE in MCC environment is non-trivial because mobile devices are resource-constraint (such as limited battery, limited computing power) and they are not suitable to run such high complexity of crypto operation. This research issue is unmatured and requires in-depth investigation. In this paper, we investigate the research problems and challenges of data access control in mobile cloud computing (MCC). Specifically, we conduct the comprehensive review of existing solutions based on CP-ABE. The review is done through the analysis and discussion of recent solutions. We also provide the comparative analysis of the selected works. Finally, we point out possible research issues and challenges.
Long-term Head Pose Forecasting Conditioned on the Gaze-guiding Prior

Forecasting head pose future states is a novel task in computer vision. Since future may have many possibilities, and the logical results are much more important than the impractical ones, the forecasting results for most of the scenarios should be not only diverse but also logically realistic. These requirements pose a real challenge to the current methods, which motivates us to seek for better head pose representation and methods to restrict the forecasting reasonably. In this paper, we adopt a spatial-temporal graph to model the interdependencies between the distribution of landmarks and head pose angles. Furthermore, we propose the conditional spatial-temporal variational graph autoen-coder (CST-VGAE), a deep conditional generative model for learning restricted one-to-many mappings conditioned on the spatial-temporal graph input. Specifically, we improve the proposed CST-VGAE for the long-term head pose forecasting task in terms of several aspects. First, we introduce a gaze-guiding prior based on the physiology. Then we apply a temporal self-attention and self-supervised learning mechanism to learn the long-range dependencies on the gaze prior. To better model head poses structurally, we introduce a Gaussian Mixture Model (GMM), instead of a fixed Gaussian in the encoded latent space. Experiments demonstrate the effectiveness of the proposed method for the long-term head pose forecasting task. We achieve superior forecasting performance on the benchmark datasets compared to the existing methods.
<scp>FADE</scp>: A forensic image generator for android device education

Abstract Realistic case studies are essential to training successful digital forensics examiners. However, the generation of realistic datasets is time‐consuming and resource taxing. This paper presents a technical solution that populates Android emulators with realistic mobile forensic data. The emulator's data can be extracted into a raw disk image that is usable in mobile forensic training scenarios. In addition, the tool allows a user to populate the Android emulators with custom text messages, phone contacts, phone calls, and files. This population task is achieved by utilizing the Android Debug Bridge, Android Content Providers, SQLite databases, and the NodeJS runtime environment. This paper presents the software design and development, the requirements and limitations, and the testing process implemented in this research. The contribution of this paper is twofold. First, it identifies potential data and mechanisms to generate Android mobile forensic datasets using customized data population. Second, it creates a foundation for future research on the topic of mobile forensic emulators for training purposes. This article is categorized under: Digital and Multimedia Science &gt; Mobile Forensics Crime Scene Investigation &gt; Education and Formation
The Theoretical Breakthrough of Self-Supervised Learning : Variational Autoencoders and Its Application In Big Data Analysis

Abstract In the study of big data statistics, a large number of unknown latent variables bring great difficulties to modeling. Variational autoencoders(VAE) can overcome the shortcomings of traditional variational methods such as low efficiency and poor generality, and provide an efficient and extensible framework for variational posteriori inference and approximate maximum likelihood learning based on gradient. On the basis of reviewing the development history of variational autoencoders and taking the deep latent variable model(DLVM) as an example, this paper introduces the basic principle of variational autoencoders and analyzes its application under the background of big data. The problems in theory and application of variational autoencoders are presented, as well as the topics to be further studied. The combination of variational autoencoders and other statistical modeling methods may become a new idea for big data research.
CAvSAT: Answering Aggregation Queries over Inconsistent Databases via SAT Solving

Consistent Query Answering (CQA) is a rigorous and principled approach to answering queries posed against inconsistent databases. Computing consistent answers to a Select-Project-Join (SPJ) query or an SPJ query with aggregation operators on a given inconsistent database can be an intractable problem. We demonstrate CAvSAT, a system for CQA that leverages a set of natural reductions from a given CQA instance to boolean Satisfiability (SAT) and its optimization variants. CAvSAT is the first system that is capable of handling unions of SPJ queries with aggregation operators SUM and COUNT, and databases that are inconsistent w.r.t. key constraints, functional dependencies, and denial constraints.
New possible applications of the augmented-reality in urban design

Abstract Nowadays, the augmented-reality, usually called as AR, means the overlay of virtual item with a really existing scene. It has been widely used in some areas, like movie industry and design. As for architects, some will use it in the process of design. Instead, the augmented-reality can also be used in urban planning and design. For example, the augmented-reality can overlap the information of the stores with the real situation, and people can easily find the place they need to go to. Also, the augmented-reality can also used to help the developers to collect the information from people, and obtain people’s demand for urban development.