Reviewerly/exHarmony huggingface dataset:
A Simple Approach to Classify Fictional and Non-Fictional Genres

In this work, we deploy a logistic regression classifier to ascertain whether a given document belongs to the fiction or non-fiction genre.For genre identification, previous work had proposed three classes of features, viz., low-level (character-level and token counts), high-level (lexical and syntactic information) and derived features (type-token ratio, average word length or average sentence length).Using the Recursive feature elimination with cross-validation (RFECV) algorithm, we perform feature selection experiments on an exhaustive set of nineteen features (belonging to all the classes mentioned above) extracted from Brown corpus text.As a result, two simple features viz., the ratio of the number of adverbs to adjectives and the number of adjectives to pronouns turn out to be the most significant.Subsequently, our classification experiments aimed towards genre identification of documents from the Brown and Baby BNC corpora demonstrate that the performance of a classifier containing just the two aforementioned features is at par with that of a classifier containing the exhaustive feature set.
Classification Method of Encrypted Traffic Based on Deep Neural Network

With the widespread use of network traffic encryption technology, the traditional traffic classification method has gradually become invalid, which increases the difficulty of network management and poses a serious threat to network security. This paper analyzes the traffic encrypted and transmitted by VPN and explores its classification method. By extracting the timing characteristics of the encrypted traffic, the classification model of the deep neural network was used to classify the traffic of seven different categories in the encrypted traffic, and compared with the commonly used naive Bayesian classification algorithm. At the same time, the batch size that affects the training of deep neural network models was studied. Experiments show that the classification ability of encrypted traffic classification model based on deep neural network is much better than the naive Bayesian method. During training, the batch size has different effects on the deep neural network model. When the batch size is 40, the deep neural network model has the best classification ability.
An Enriched Chaotic Quantum Whale Optimization Algorithm Based Job scheduling in Cloud Computing Environment

In recent years, cloud has become a metaphor for voluminous data storage and utilization of virtual resources by cloud user.This study focuses on independent job scheduling in cloud computing paradigm.This paper devised a new enriched approach of chaotic quantum whale optimization algorithm (CQWOA), whose ultimate objective is to overwhelm degree of imbalance, increasing makespan and overheads in cost, energy consumption, resource utilization.With the intelligence of chaotic mapping and the quantum mechanism based optimal virtual machine selection, the global optima is achieved more significantly by CQWOA.This algorithm discovers best location and the direction to detect appropriate virtual machine in terms of reduced resource utilization, increasing makespan and evenly distributing the work load, makes the presented model to be more superior than Particle swarm optimization, Ant colony Optimization and standard Whale optimization.The existing models fails to handle the inconsistencies and vagueness in discovering potential virtual machine's which qualifies their requirements and standard whale optimization easily meets earlier converge of local optima and it is very complex for them to reach global best virtual machines in cloud computing Paradigm.The proposed CQWOA model has saved the total execution cost in job scheduling more successfully and it is proved by its simulation results.
Enhancing face pose normalization with deep learning

In this study, we propose a hybrid method for face pose normalization, which combines the 3-D modelbased method with stacked denoising autoencoder (SDAE) deep network.Instead of applying a mirroring operation for the invisible face parts of the posed image, SDAE learns how to fill in those regions by a large set of training samples.In the performance evaluation, we compare the proposed method to four different pose normalization methods and investigate their effects on facial emotion recognition and verification problems in addition to visual quality tests.Methods evaluated in the experiments include 2-D alignment, 3-D model-based method, pure SDAE-based method, and generative adversarial network-based normalization method.Experiments performed on Multi-PIE dataset show that the proposed method produces visually reasonable results and outperforms the others in facial emotion recognition.On the other hand 2-D alignment is sufficient in the verification problem where the detailed face characteristics should be preserved in the normalization process.
A diagnosis optimization system for grain processing based on multiple data analysis algorithms

Currently, the grain processing industry excessively pursues the goal of fine processing, resulting in serious waste of grain processing enterprises, increasing raw material cost and low machining efficiency, etc. To solve problems, this paper builds a food processing and data analysis system which can perform various data analysis. The system has following functions: Grain processing database is able to store and call consumer market surveys and data of grain processing enterprises, visualizing the national diet and grain processing process. Data analysis can carry out statistical analysis of data sets extracted according to required indicators, diagnostic optimization based on Data Envelopment analysis, prediction analysis based on curve fitting, classification analysis based on clustering algorithm, etc. It can obtain conclusions of relationships between the output rate, loss rate and unit power consumption of each link, and influencing factors of grain processing links like rice milling and polishing on products. The diagnostic evaluation system can evaluate consumption of raw materials and power for different product enterprises, and improve the scheme to avoid loss caused by the mismatch between raw materials and process in production process. The production practice shows that the grain processing data analysis system designed in this paper is effective.
A Holistic View of Finite Populations for Determining an Appropriate Sample Size

This study presents practical and easy-to-implement approaches for determining appropriate, or “safe”, sample sizes for routinely conducted statistical surveys. Finite populations are considered holistically and independently of whether they are continuous, categorical, or dichotomous. It is proposed that in routinely conducted sampling surveys variance-ordered categories of populations should be the basis for calculating the safe sample size given that the variance within a target population is a primary factor in determining sample size a priori. Several theoretical and operational justifications are presented for this thesis. Dichotomous populations are often assumed to have higher variances than continuous populations when the latter have been standardized and have all values in the interval [0 1]. Herein, it is shown that this is not a valid assumption; a significant proportion of dichotomous populations have lower variances than continuous populations. Conversely, many continuous populations have variances that exceed the limits that are broadly assumed in literature for determining a safe sample size. Finite populations should thus be viewed holistically. A simple first step is to partition finite populations into just two categories: convex and concave. These two categories are relative to a flat population with a known variance as the threshold between them. This variance is used to determine a safe sample size for any continuous population with a flat or positive curvature, including approximately 20% of dichotomous populations. For all other populations the value of 0.25 is recommended for approximating the actual population variance as the primary parameter for sample size determination. The suggested approaches have been successfully implemented in fisheries statistical monitoring programmes but it is believed that they are equally applicable to other applications sectors.
Cryptocurrency and Blockchains: <i>Retail to Institutional</i>

A reduction in cost of traditional financial intermediation was one of the main motivations cited by Satoshi Nakamoto in a 2008 proposal for “… an electronic payment system based on cryptographic proof instead of trust.” We begin here with some back-of-the-envelope calculations of these potential cost savings and benefits from the customer perspective. We then discuss the public blockchain ledger and various solutions to two important problems that are constraints on the public blockchain’s trustless consensus, viz. “mining” costs in proof-of-work and governance issues. We speculate that foreseeable institutional implementations will often involve integration of permissioned blockchains with public blockchains. We then discuss exchanges for trading cryptocurrencies, the second component of the crypto blockchains, and in particular their “teething problems,” along with the evolution of a subset of them into increasingly “industrial strength” entities. We suggest that with a more industrial strength infrastructure in place, self-executing smart contracts are virtually natural counterparts for more traditional passive investment products. We end with a discussion of Security Token Offerings (STOs) and the newer Initial Coin Offerings (ICOs): STOs are an interesting hybrid between the ICOs and traditional IPOs; they could conceivably pave the way to a long-time-coming “direct electronic IPO” market. <b>TOPICS:</b>Currency, indexing exchange-traded, exchanges/markets/clearinghouses <b>Key Findings</b> • A set of crypto exchanges is evolving into institutional-strength entities with law-of-one-price, clearing and settlement, custody services, and user algorithms to deal with <i>fake</i> inflated trade volumes. • Pure public blockchains with well-known “blockchain trilemma” problems like high energy consumption, low performance, governance shortcomings, and no clear “how-to-pay” mechanism for ledger-updating in non-crypto applications are inexorably leading toward hybrid public/permissioned blockchain networks. • Despite their rocky start, STOs and ICOs are leading to direct or electronic IPO markets while smart contracts running on blockchains with securities trading identifiers and data seem ideal vehicles for administering passive investment products.
RGB Inter-Channel Measures for Morphological Color Texture Characterization

The perception of textures is based on high-level features such as symmetry, brightness, color or direction. Texture characterization is a widely studied topic in the image processing community. The normalized volume of morphological series is used as a texture descriptor in RGB images. However, the correlation between different color channels is not exploited with this descriptor. We propose the usage of inter-channel measures in addition to the volume, to enhance the descriptors potential to discriminate textures. The experiments show that standard texture classification techniques increase between 3%–10% in performance when using our descriptor instead of other state of the art descriptors that do not use inter-channel measures.
Development and Validation of Bone Models using Structural Dynamic Measurement Methods

Abstract Vibration measurement and signal analysis methods are common to evaluate the functionality and characteristics of technical components in different industrial and scientific areas. Modal analysis for example is a standard method to characterize the dynamic behavior of a structure and enables the development of validated bone models. The state of the art of analyzing bone structures does not include the modal damping, although it has a significant influence on the dynamic characteristics. Within the presented investigations, the modal analyses have been performed contactless with respect to excitation and response acquisition, which implies that there are no influences of shakers or sensor couplings. Therefore, an automatic impulse hammer and a 3D Scanning Laser Doppler Vibrometer were used for excitation and response detection. Various supports of the test specimens, surface pretreatments, excitation points and excitation impulses were examined to optimize the measurement setup and process. Experimental modal analysis data were analyzed by curve fitting methods to determine the modal parameters. To evaluate different structures and effects of damping, 3D printed artificial bones and animal in vitro bones were used to perform the measurements. To produce the cortical layer of the artificial bone models, volume models were generated based on medical image data and printed by polyamide-based selective laser sintering. The cancellous bone was represented by different foam fillings for the artificial bones. Thereby, the variation of the porosity was achieved by using different mixing ratios of polyurethane foam and hardener. Furthermore, the modal damping parameters were determined from the measurement of animal bones. The measurement time was optimized during the practical implementation of the parameter determination to minimize the influence of drying and decomposition processes on the measurement results.
Impact of an augmented reality system on students' learning performance for a health education course

In recent years, the innovative applications of integrated augmented reality (AR) into educational settings had increased rapidly in Taiwan.AR provides the scene of the real world while interacting with the virtual and physical objects and increases students' learning motivation.This paper presents an empirical study that investigated the middle-school students learning health education courses related to human body structure knowledge integrating AR technology with different learning style preferences.A two-way t-test was applied to examine the experimental (AR-based) or control (traditional lecture) group in health education courses for four consecutive weeks, comparing with pre-test and post-test scores.The findings revealed that students in the AR experimental group apparently had a higher learning achievement than the students in the control group.Moreover, students with different learning styles had the significant differences in learning achievement.The research outcomes provided the practical implications for educators in incorporating AR applications for enhancing learning motivation and to study the differences in students' learning style.
Create your own agile methodology for your research and development team

Agile methodologies conquer space beyond their industrial use.For applying them in a situation other than classic software development, one should first assess the features of this specific environment.As a next step, elements from various well-known agile methodologies (roles, events, products) can be considered as building blocks.These elements combined in a new way, adopted appropriately result in a specific, own agile methodology.In this study, we present a list of aspects that one should consider when creating a specific agile methodology for a R&D team.Our own agile methodology created for the txtUML R&D team from the Faculty of Informatics of Eötvös Loránd University is built along this list of aspects.Known and new agile elements were included in this specific methodology and are explained in this article in detail.The txtUML R&D methodology has been used with satisfaction since 2018, as evidenced by backward surveys.
Investigating diffuse irradiance variation under different cloud conditions in Durban, using k-means clustering

Diffuse irradiance is important for the operation of solar-powered devices such as photovoltaics, so it is important to analyse its behaviour under different sky conditions. The primary cause of short-term irradiance variability is clouds. One approach to analyse the diffuse irradiance variation is to use cluster analysis to group together days experiencing similar cloud patterns. A study was carried out to examine the application of k-means clustering to daily cloud data in Durban, South Africa (29.87 °S; 30.98 °E), which revealed four distinct day-time cloud cover (CC) patterns classified as Class I, II, III and IV, corresponding to cloudy, sunny, or a combination of the two. Diffuse irradiance was then correlated with each of the classes to establish corresponding diurnal irradiance patterns and the associated temporal variation. Class I had highest diffuse irradiance variation, followed by Classes III, IV and II. To further investigate the local cloud dynamics, cloud types were also analysed for Classes I−IV. It was found that stratocumulus (low cloud category); altocumulus translucidus, castellanus and altocumulus (middle cloud category); and cirrus fibrates and spissatus (high cloud category), were the most frequently occurring cloud types within the different classes. This study contributes to the understanding of the diurnal diffuse irradiance patterns under the four most frequently occurring CC conditions in Durban. Overall, knowledge of these CC and associated diffuse irradiance patterns is useful for solar plant operators to manage plant output where, depending on the CC condition, the use of back-up devices may be increased or reduced accordingly.
MixAR

MixAR, a full-stack system capable of providing visualization of virtual reconstructions seamlessly integrated in the real scene (e.g. upon ruins), with the possibility of being freely explored by visitors, in situ, is presented in this article. In addition to its ability to operate with several tracking approaches to be able to deal with a wide variety of environmental conditions, MixAR system also implements an extended environment feature that provides visitors with an insight on surrounding points-of-interest for visitation during mixed reality experiences (positional rough tracking). A procedural modelling tool mainstreams augmentation models production. Tests carried out with participants to ascertain comfort, satisfaction and presence/immersion based on an in-field MR experience and respective results are also presented. Ease to adapt to the experience, desire to see the system in museums and a raised curiosity and motivation contributed as positive points for evaluation. In what regards to sickness and comfort, the lowest number of complaints seems to be satisfactory. Models' illumination/re-lightning must be addressed in the future to improve the user's engagement with the experiences provided by the MixAR system.
Under Water Object Detection Based on Convolution Neural Network

Traditional underwater image processing methods have low accuracy and slow recognition speed. Underwater multi-target recognition is an important part of multi-target tracking. To solve this problem, an underwater multi-target detection method based on multi-scale convolution neural network (MSC-CNN) is proposed. Firstly, according to the problem of low contrast caused by the absorption and attenuation of underwater light, using the attenuation theory to equalize the dark channel of image to reduce the interference noise of background light in order to enhance the feature signal of object. Secondly, using MSC-CNN to fuse the feature maps generated by different convolution layers in the form of down-sampling and 1*1 convolution to extract the feature. Using the idea of Faster R-CNN for reference, the region-of-interest network is used to select regions of interest and share convolution layers. Finally, Softmax Loss is used to calculate classification probability and non-maximum suppression to correct the border position. The accuracy and test time are taken as evaluation metrics, and the recognition effect of different methods for underwater image is analyzed by comparing with the existing top algorithms. The experimental results of underwater images show that this method has advantages under various evaluation metrics.
Improved Virus Optimization algorithm for two-objective tasks Scheduling in Cloud Environment

Cloud computing is increasingly recognized as a new way to use on-demand, computing, storage and network services in a transparent and efficient way.The development of applications in cloud environments is faced with the need to efficiently schedule a large number of tasks and resources.However, in the most of the time, the resources in cloud are not efficiently utilized due to inadequate scheduling task algorithm in virtual machines.Therefore, task scheduling is one of the most challenging issues in cloud computing.In this paper, we propose two-objective virus optimization algorithm of the makespan and the cost, for mapping tasks to virtual machines in order to meet the needs of cloud service quality and proper assignment of resources.Thus, based on genetic algorithm some parameters of Virus optimization algorithm are redefined to strengthen sorting ability between virus infection strategies.Our combined methods aims to improve the performance of scheduling algorithms.It outperforms some existing approaches for task scheduling in Cloud computing.
Counting Instances of Objects Specified By Vague Locations Using Neural Networks on Example of Honey Bees

This article presents a novel approach to segmentation and counting of objects in color digital images.The objects belong to a certain class, which in this case are honey bees.The authors briefly present existing approaches which use Convolutional Neural Networks to solve the problem of image segmentation and object recognition.The focus however is on application of U-Net convolutional neural network in an environment where knowledge about the object of interest is only limited to its rough, single pixel location.The authors provide full access to the details of the code used to implement the algorithms, as well as the data sets used and results obtained.The results show an encouraging low level of counting error at 14.27% for the best experiment.
Vision-only 3D Tracking for Self-Driving Cars

A vision-only tracking framework is developed and experimentally evaluated for self-driving cars. Vision-only object detection is achieved by transforming stereo depth maps into point clouds, followed by LiDAR-based detectors. Each detection yields location, orientation, and object size. A tracking algorithm is then used to combine the detections with a physics based model to create robust vehicle tracks and IDs. We empirically evaluate our approach to the ones relying on LiDAR using the KITTI Tracking dataset. We found that vision-only trackers yield comparable performance in short ranges, but are still outperformed by the LiDAR-based one at far distances. Specifically, vision-only detection and tracking can generate good estimates achieving close performance to LiDAR based detection at close range. The approach is generalizable to other trackers, particularly those which use multiple sensors.
Depth Map Improvements for Stereo-based Depth Cameras on Drones

Using stereo-based depth cameras outdoors on drones can lead to challenging situations for stereo algorithms calculating a depth map.A false depth value indicating an object close to the drone can confuse obstacle avoidance algorithms and lead to erratic behavior during the drone flight.We analyze the encountered issues from real-world tests together with practical solutions including a post-processing method to modify depth maps against outliers with wrong depth values.