Reviewerly/exHarmony huggingface dataset:
Correction Method of Step Voltage Considering Equivalent Circuit of Human Body

The value of the step voltage affected by human parameters and soil parameters will mislead the personal safety risk assessment model. This paper studies the influencing factors of the step voltage based on the step voltage test and compare the difference between the equivalent circuits with and without human body resistance, which gives a human body influence coefficient k: The ratio of the step voltage with or without the human body equivalent circuit. The formula of k about soil resistivity, skin resistance, electrode radius and burial depth were fitted by FEM simulation data. The error of the expression was verified by the FEM analysis result that the error was not more than 5% (allowable value range). The study draws the conclusion that the step voltage is significantly reduced when the human body equivalent circuit exists. The fitting formula shows that the reduction in areas with high soil resistivity is greater than that in low. The conclusion provides data support to Step voltage monitoring alarm.
Reader service experience system of intelligent library based on artificial intelligence and machine learning

With the development of social economy and the improvement of science and technology, digital video on the Internet is increasing rapidly, and it has become a new force to promote the development of the times. Most of these videos are stored in the memory, which poses a great challenge to the research and development of the system. The reader service system is an important part of library service. The library uses it to collect information resources, not just for service and work. The document combines the video of library service, the analysis of video recovery and video software requirements of digital library, puts forward the design goal and conception of video search, and puts forward a foundation. From the video data of digital library, video retrieval experiments are gradually carried out. These experimental results show that the number of enhanced dynamic clustering algorithm increases to ensure the complexity of the image.
Design of state Grid shopping mall heating technology application assistant decision system based on big data analysis

In view of the problem that the decision-making system currently in use takes a long time to respond and causes energy waste due to its low data processing capacity, the state Grid mall heating technology application assistant decision-making system based on big data analysis was designed. The data acquisition and processing part of the original hardware is optimized, and the collected data is reduced and discretized in the software part of the system. Big data analysis technology is used to realize the function of assistant decision system after data analysis. The experimental results show that it is verified that the designed system has short decision response time, high efficiency and can effectively reduce energy waste.
Estimation of Biresponse Multipredictor Model using Local Linear Estimator in Case of Scholastic Aptitude and Islamic Tests Modeling

Abstract Scholastic aptitude test (SAT) and Islamic test are tests that used by state Islamic colleges (SIC) in Indonesia to select new students on the admission test. Both of them are taken from the same subjects, so they are correlated each other. The behavior patterns of both test scores are change because they were observed from 2016 to 2018. In that period, SAT and Islamic test scores of SIC in western Indonesia are higher than those in eastern Indonesia. So the locally modeling is appropriate for this case. There is a significant correlation between both of the tests then they can be modeled simultaneously by using biresponse nonparametric model. In this research, estimating parameter of biresponse multipredictor nonparametric regression model by using weighted least square method. The results show that in average, the increasing public senior high schools percentage and first choice percentage would increase the SAT scores and Islamic test scores of SIC in western Indonesia. Otherwise, in eastern Indonesia the increasing public senior high school percentage and first choice percentage would decrease the SAT scores and Islamic test scores. It means that there are different patterns of SAT and Islamic test scores between western Indonesia and eastern Indonesia.
Chat-Oriented Dialogue System That Uses User Information Acquired Through Dialogue and Its Long-Term Evaluation

A chat-oriented dialogue system can become more likeable if it can remember information about users and use that information during a dialogue. We propose a chat-oriented dialogue system that can use user information acquired during a dialogue and discuss its effectiveness on long-term interaction. In our subjective evaluation over five consecutive days, we compared three systems: a system that can remember and use user information over multiple days (proposed system), one that can only remember user information within a single dialogue session, and another that does not remember any user information. We found that users were significantly more satisfied with our proposed system than with the other two. This paper is the first to verify the effectiveness of remembering on long-term interaction with a fully automated chat-oriented dialogue system.
High-Scale Food Recommendation Built on Apache Spark using Alternating Least Squares

Recommendation system is an information filtering application which anticipates the user's ratings by considering their previous judgements. These systems are an extensive aspect of most state-of-the-art applications. The highly accessible data on the internet is playing a dominant role in these systems. Collaborative filtering is one of the mechanisms of building a recommendation system which when fabricated with the ALS algorithm promises stunning results. This is because of the two-step iterative matrix factorisation approach of ALS. This recommendation system has been built using Amazon food product reviews on Apache Spark by servicing a master and few slave nodes. PySpark's ml library has been used for constructing the ALS model and RDD were utilised for handling the colossal amount of data. Composing the model over 50 ranks i.e., the total count of features chosen. Then the top 3 products which the user might prefer will be recommended.
Terms Extraction from Clustered Web Search Results

With a significant increase in web content, users cannot find easily the exact information that needs, so users are forced to read many search results sequentially until they reach what they want. In this paper, we proposed an approach to extract terms that represent sets of search results. These terms help users to access what they are looking for quickly and easily. Experimental results show that our proposed approach efficiently extract terms that represent these sets.
Where does Everyday Design Innovation come from?: Case studies in IKEA Product Hacking

Background Design is no longer owned only by skilled designers.Product hacking, which has to emerge as a major phenomenon in industrial and consumer products, is an everyday design activity aimed at remaking and remanufacturing existing products that provide new meanings.This paper aims to contribute to identifying design innovation within cases of product hacking as examples of everyday design. MethodsProduct hacking workshops were conducted to explore the potential of conceptual blending as means to identify innovation in everyday design activity. ResultsThrough a trial and error morphological approach, participants appeared to arrive at Aha! moments that then acted as a catalyst for the identification of a feasible blend possibility.Rather than the explicit operation or application of meaning blends in product hacking, participants relied upon the juxtaposition of existing products as both opportunity and constraint on product combination and meaning change.Conclusions Conceptual meaning blending may have the potential as means to identify and assess innovation in everyday design activity, including product hacking.However, results indicate that meaning blending and their meanings were not an explicit approach to meaning innovation through the blending of existing products.
International experience of territorial-spatial organization of university campuses in urban structure

Universities are important structural and functional elements of megalopolises, they carry out professional training of specialists for various spheres of society; in addition, they are scientific and cultural centers. Nowadays the issues of education improvement, in particular, its spatial organization, are especially important. In this regard, this work analyzes international experience of spatial organization of university campuses in urban structure, including determination of major forms of territorial-spatial organization of university campuses and approaches to territorial-spatial development of campuses both in urban structure and in the campus territory.
Artificial Intelligence Technology in Computer Network Under the Background of Internet

With the rapid development of national economy and the continuous progress of information technology, computer technology has been widely used in all walks of life and people’s daily work and life. The number of domestic electronic computers and the development level of the Internet are continuously improving. The emergence of artificial intelligence (AI) and the emergence of new systems and new products under the influence of a series of AI have significantly changed people’s understanding and cognition. Based on this, this paper carries out the exploration and research of AI technology in computer network under the background of Internet. This paper analyzes the application of AI in computer, and explores its specific application in computer. Finally, it puts forward reasonable suggestions for the application strategy of AI in computer. It is found that in the development of computer, intrusion detection technology, intelligent firewall technology and intelligent anti-spam technology account for 26%, 25% and 25% respectively in the application of computer, which can greatly improve the security and stability of network system operation. With the continuous innovation and reform of Internet technology, AI service in computer is expected to become a new application widely accepted by people.
Investigation on enhancing the binary classification accuracy of supervised classifiers using various transforms

Abstract The Classification is used for testing instances where the unknown class labels are assigned where the predictor features are known. This paper aims to investigate the classification performance improvement of popular supervised classification approaches using data transformation techniques. Hilbert Transform, Discrete Wavelet Transform, and Principal Component Analysis are investigated as data transformation techniques for improving the performance of four different supervised classification approaches namely K-Nearest Neighbor classifier, Random Forest Classifier, Naive Bayes Classifier, and Support Vector Machine. SONAR dataset is used in this research work and the highest Mathews Correlation Coefficient of 0.72 is attained for Random Forest Classifier.
Tidy-up Task Planner based on Q-learning

A central problem in learning in complex environments is balancing exploration of untested actions against exploitation of actions that are known to be good.The benefit of exploration can be estimated using the classical notion of Value of Information-the expected improvement in future decision quality that might arise from the information acquired by exploration.Estimating this quantity requires an assessment of the agent's uncertainty about its current value estimates for states.In this paper, we adopt a Bayesian approach to maintaining this uncertain information.We extend Watkins' Q-learning by maintaining and propagating probability distributions over the Q-values.These distributions are used to compute a myopic approximation to the value of information for each action and hence to select the action that best balances exploration and exploitation.We establish the convergence properties of our algorithm and show experimentally that it can exhibit substantial improvements over other well-known model-free exploration strategies.
Effects of Visual Flow Alterations on Psychophysiological Responses to Virtual Reality Exercise

Virtual reality (VR) technology combined with exercise, called VR exercise, is believed to have beneficial effects on mood; but VR factors contributing to improved mood remain ambiguous. The purpose of this study was to examine the effect of visual flow speed on psychophysiological responses (i.e., physiological responses, ratings of perceived exertion or RPE, and mood) to immersive VR exercise in a simulated natural environment. Eighteen male participants (Mage =23.1, SD = 1.9 years) cycled an ergometer at 80 watts for 5 minutes on three separate occasions while watching a first-person VR movie through VR goggles at three different speeds of visual flow, corresponding to 7.5 km.h-1, 15 km.h-1, and 22.5 km.h-1. The order of the three speeds was randomized in a counterbalanced design. We measured heart rate, oxygen uptake, minute ventilation, respiratory rate, and cadence during the exercise, and we recorded ratings of perceived exertion (RPE) and mood immediately after the exercise. We evaluated mood states with the Two-Dimensional Mood Scale. One-way repeated measures analysis of variance or the Friedman test revealed no significant effects on any physiological variables, RPE or cadence as a result of altered visual flow speed during VR exercise (p > .05). However, speed of visual flow significantly influenced participant ratings of Vitality (p = 0.01) and Pleasure (p = 0.02), with the faster speed resulting in a more positive mood state. As these findings showed that VR exercise with faster visual flow induced positive mood states, we recommend faster visual flow to induce better mood states in VR exercise.
LibGuides: Blackface Minstrelsy Resources: Images

This guide has been developed by The Center for American Music to provide resources for researching blackface minstrelsy and other demonstrations of blackface performance.
A Skeleton and Visual Tracking Fusion Based Person-Following System for Mobile Service Robots

To overcome the problems of human skeleton loss and body occlusion in a person following system, this paper proposes a real-time and reliable control method for mobile service robots, which combines human skeleton tracker and the discriminative correlation filter with channel and spatial reliability (CSR-DCF) visual tracker. In particular, the CSR-DCF visual tracker is responsible for correcting and locking the special person when the human skeleton tracker provided by Orbbec SDK becomes unstable under the moving mobile service robot. While the skeleton tracker is responsible for taking over the tracking task when the CSR-DCF visual tracker drifts to non-skeleton regions. Experiment results show that the total accuracy has risen to 84.3%, which verifies the effectiveness of the proposed method.
Fitting the space line of high repeatability to noisy point cloud data

Abstract The spatial line fitting algorithm with high repeatability for noise point cloud data is challenging and it is of great significance for high-precision measurement, visual guidance of robotics and CNC machining, but little study has been done at this topic. In this paper, a new algorithm is proposed to fit the spatial line with high repeatability to adapt to the point cloud data with noises. This method combines the idea of repeated residuals and the least square algorithm to filter out the noises and then solve the problem. The algorithm can successfully filter the noises. Four popular straight lines fitting algorithms are implemented as benchmarks. A large number of experimental results show that this algorithm can obtain highly repeatable and accurate straight lines.
Automated Census Population Projection and Data Management System

Population census data has become more pervasive to lives which has demanded for increase in scope, completeness, accuracy and validity, and improve on their national value and international comparability. The accuracy of population projections has been attracting more attention, driven by concerns about the possible long-term effects of aging and other demographic trends. This paper therefore, attempt to improve on the manual way of managing census data by developing an automated census data management system. This system in addition, is capable of projecting population growth using exponential growth equation. The National Population Commission, Benin City was used as a case study.
Funding for lending schemes should prioritize SME lending

In the aftermath of the sovereign debt crisis the Central Bank of Hungary implemented a great-scale funding for lending scheme designed specifically to subsidize SME finance. This creates a unique opportunity to identify this policy in the SVAR framework as asymmetric credit supply shocks specific to SME lending. I find that during the post-crisis recovery, such disturbances had a substantial effect on lending conditions and the real economy. Moreover, rather than supplanting lending to large enterprises, the program had considerable positive spillover effects to this sector. Finally, for a unit of lending, these shocks had larger and more persistent effect on output than general credit supply shocks. These results are robust to different proxies of economic performance and alternative identification strategies. I conclude that under tight lending conditions funding for lending schemes are more effective if concentrated to SMEs.
Influence of the binder used in pressing ash for laser sampling on the quality of analytical signals of the elements detected

This work is devoted to determining the chemical composition of coal ashes. We used the optical emission spectrometry with inductively coupled plasma and laser sampling as a research method. The aim of the work is to determine the limits of detection (LoDs) and quantitative determination (LoQs) of elements in ashes using polyvinyl alcohol (PVA) and microcrystalline cellulose (MCC) as binders. In this work, analytical signals of Al, Ba, Be, Ca, Cr, Cu, Fe, K, La, Li, Mg, Mn, Na, Ni, P, Pb, S, Sc, Sr, Ti, Y, V, Zr, and Zn were analyzed in tablets obtained from the standard samples ZUK-1, ZUK-2, SG-1A, SG-3, SGD-2A, and SO-1, compacted using PVA and MCC, as well as from binding agents themselves. To obtain compact materials based on fly ash and geological materials, both binders are suitable if their chemical purity is enough for analysis. However, when using both MCC and PVA, some drawbacks were identified. The intensities of analytical lines of the elements were determined lower during the ablation of compact ashes with MCC than with PVA. The signal-background ratio is lower when using MCC as a binder material compared with compacts based on PVA. However, the highest grade PVA according to GOST 10779-78, used in the work, contains an admixture of sodium, about 0.06 w. %, which interferes with quantitative measurements if the sodium content in the sample is comparable to this value, which can be extremely rare in the analysis of fly ashes.
Terminal Area Capacity Assessment Under Military Activities Based on Improved Genetic Algorithm

Accurately predicting the airspace capacity under the influence of military activities is of great significance for easing the contradiction between military and civil aviation. In the paper, an improved genetic algorithm is used to solve the bottleneck of terminal capacity affected by military activities. The corresponding population generation method, encoding and decoding method, fitness calculation method and crossover variation method are designed in the algorithm. Simulation shows that the method can not only avoid the limitation of exponential increase of computation amount of traditional algorithm, but also improve the efficiency of calculation.
Perspectives of Blockchain in Cybersecurity

In recent years, blockchain has emerged as a popular data structure for use in software solutions. However, its meteoric rise has not been without criticism. Blockchain has been the subject of intense discussion in the field of cybersecurity because of its structural characteristics, mainly the permanency and decentralization. However, the blockchain technology in this field has also received intense scrutiny and caused to raise questions, such as, Is the application of blockchain in the field simply a localized trend or a bait for investors, both without a hope for permanent game-changing solutions? and Is blockchain an architecture that will lead to lasting disruptions in cybersecurity? This chapter aims to provide a neutral overview of why blockchain has risen as a popular pivot in cybersecurity, its current applications in this field, and an evaluation of what the future holds for this technology given both its limitations and advantages.
LBNN: Perceiving the State Changes of a Core Telecommunications Network via Linear Bayesian Neural Network

The core network is the most basic facility in the entire telecommunications network, which is consists of large number of routers, switches and firewalls. Network management like re-planning routes or adjusting policies is very important to avoid failures. However, the timing of intervention is very challenging. Too early intervention will incur unnecessary overheads, and too late intervention will cause serious disaster. In this paper, we analyzed a large data set from a real-world core telecommunications network and proposed Linear Bayesian Neural Networks (LBNN) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code available at https://github.com/YanyingLin/Lbnn to perceive the core network state changes and make decisions about network intervention. In particular, we considered three aspects of complexity, including the weight of the mutual effect between devices, the dependence on the time dimension of the network states, and the randomness of the network state changes. The entire model is extended to a probability model based on the Bayesian framework to better capture the randomness and variability of the data. Experimental results on real-world data set show that LBNN achieves very high detection accuracy, with an average of 92.1%.
Self-Teaching Video Object Segmentation

Video object segmentation (VOS) is one of the most fundamental tasks for numerous sequent video applications. The crucial issue of online VOS is the drifting of segmenter when incrementally updated on continuous video frames under unconfident supervision constraints. In this work, we propose a self-teaching VOS (ST-VOS) method to make segmenter to learn online adaptation confidently as much as possible. In the segmenter learning at each time slice, the segment hypothesis and segmenter update are enclosed into a self-looping optimization circle such that they can be mutually improved for each other. To reduce error accumulation of the self-looping process, we specifically introduce a metalearning strategy to learn how to do this optimization within only a few iteration steps. To this end, the learning rates of segmenter are adaptively derived through metaoptimization in the channel space of convolutional kernels. Furthermore, to better launch the self-looping process, we calculate an initial mask map through part detectors and motion flow to well-establish a foundation for subsequent refinement, which could result in the robustness of the segmenter update. Extensive experiments demonstrate that this ST idea can boost the performance of baselines, and in the meantime, our ST-VOS achieves encouraging performance on the DAVIS16, Youtube-objects, DAVIS17, and SegTrackV2 data sets, where, in particular, the accuracy of 75.7% in J-mean metric is obtained on the multi-instance DAVIS17 data set.