Reviewerly/exHarmony huggingface dataset:
Conversion Rate Prediction via Post-Click Behaviour Modeling

Effective and efficient recommendation is crucial for modern e-commerceplatforms. It consists of two indispensable components named Click-Through Rate(CTR) prediction and Conversion Rate (CVR) prediction, where the latter is anessential factor contributing to the final purchasing volume. Existing methodsspecifically predict CVR using the clicked and purchased samples, which haslimited performance affected by the well-known sample selection bias and datasparsity issues. To address these issues, we propose a novel deep CVRprediction method by considering the post-click behaviors. After groupingdeterministic actions together, we construct a novel sequential path, whichelaborately depicts the post-click behaviors of users. Based on the path, wedefine the CVR and several related probabilities including CTR, etc., anddevise a deep neural network with multiple targets involved accordingly. Ittakes advantage of the abundant samples with deterministic labels derived fromthe post-click actions, leading to a significant improvement of CVR prediction.Extensive experiments on both offline and online settings demonstrate itssuperiority over representative state-of-the-art methods.
Parameters and Guidelines of Enforceable Information Security Management Systems

It is increasingly important for organizations to set up an Information Security Management System (ISMS) to fulfil their business interests and their legal compliance.The main purpose of these systems is to properly protect the information owned or managed by the organization.Often, the developed ISMS complies with the external regulatory environment, but contains unenforceable rules that impede work, so it is unable to fulfil its function.In order to prevent security incidents, it is not enough to ensure legal compliance.The enforceability of these policies is gaining increasing importance in order to avoid hindering work processes.This article identifies quality parameters and guidelines in order to improve quality, enable and improve enforceability of ISMS systems, in order to fulfil their purpose, mainly protection of company information assets.By adhering to these parameters and guidelines organisations can improve their ISMS systems which enforces security of their information assets.
Monte-Carlo-based uncertainty propagation in the context of Gauss–Markov model: a case study in coordinate transformation

One of the main tasks of professionals in the Earth sciences is to convert coordinates from one reference frame into another. The coordinates transformation is widely needed and applied in all branches of modern geospatial activities. To convert from one reference frame to another, it is necessary initially to determine the parameters of a coordinate transformation model. A common way to estimate the transformation parameters is using the least-squares theory within a linearized Gauss-Markov Model (GMM). Another approach arises from a numerical method. Here, a Monte Carlo Method (MCM) is used to infer the uncertainty of estimators. This method is based on: (i) the assignment of probability distributions to the coordinates in the two reference frames, (ii) the determination of a discrete representation of the probability distribution for the transformation parameters, and (iii) the determination of the associated uncertainties from this discrete representation of the estimates of the transformation parameters. In this contribution, we compare the weighted least-squares within the GMM (WLSE-GM) and the proposed method regarding the transformation problem of computing 2D similarity transformation parameters. The results show that, the transformation parameters uncertainties are higher for the LS-MC than the WLSE-GM. This is due to the fact that the WLSE-GM solution does not take into account the uncertainties associated with the system matrix. In future studies the Monte Carlo method should be applied to the nonlinear least-squares solution.
Object Detection Based on DenseNet and RPN

Object detection algorithm based on depth model has achieved state-of-the-art results on various challenging benchmarks. However, the large amount of parameters of depth model means a large amount of calculation. This seriously limits the practical application of object detection algorithm, especially on embedded devices with limited computing power. We propose an object detection algorithm based on DenseNet and Region Proposal Network(RPN) and replace ROI Pooling with ROI Align. From the evaluation on PASCAL VOC and MS COCO we can see that the algorithm achieves object detection with fewer parameters while maintaining or improving accuracy. This is meaningful for the development of embedded in-depth learning. Finally, we explore the influence of different shared convolutional layers on object detection algorithm.
Assessment of five different methods for the estimation of surface photosynthetically active radiation from satellite imagery at three sites – application to the monitoring of indoor soft fruit crops in southern UK

Abstract. This paper assesses several methods for the retrieval of Photosynthetically Active Radiation (PAR) from satellite imagery. The results of five different methods are compared to coincident in-situ measurements collected at three sites in southern UK. PAR retrieval methods are separated into two distinct groups. The first group comprises three methods that compute PAR by multiplying the satellite-retrieved solar broadband irradiance at the surface (SSI) by a constant coefficient. The two methods in the second group are based on more sophisticated modelling of the radiative transfer in the atmosphere involving advanced global aerosol property analyses and physically consistent total column water vapour and ozone produced by the Copernicus Atmosphere Monitoring Service (CAMS). Both methods compute a cloud modification factor from satellite-retrieved SSI. The five methods have been applied to two satellite-retrieved SSI datasets: HelioClim-3 version 5 (HC3v5) and CAMS Radiation Service (CAMS-Rad). Except at the seashore site, Group 2 methods combined with the cloud extinction from the HC3v5 dataset deliver the best results with small biases of −5 to 0 µmol m−2 s−1 (−1 % to 0 % relative to the mean of the measurements), root mean square errors of 130 µmol m−2 s−1 (28 %) and correlation coefficients exceeding 0.945. For all methods, best results are attained with the HC3v5 data set. These results demonstrate that all methods capture the temporal and spatial variability of the PAR irradiation field well, although several methods require a posteriori bias adjustments for reliable results. Combined with such an adjustment, the Udo et Aro method is a good compromise for this geographical area in terms of reliability, tractability and its ability to run in real-time. Overall, the method performing a spectral discretization in cloud-free conditions, combined with the HC3v5 dataset, outperforms other methods and has great potential for supporting an operational system.
Text Extraction from Natural Scene Images using OpenCV and CNN

The influence of exponentially increasing camera-embedded smartphones all around the world has magnified the importance of computer vision tasks, and gives rise to a vast number of opportunities in the field.One of the major research areas in this field is the extraction of text embedded in natural scene images.Natural scene images are the images taken from a camera, where the background is random, and the variety of colors used in the image may be diverse.When text is present in such type of images, it is usually difficult for a machine to detect and extract this text due to a number of parameters.This paper presents a technique that uses a combination of the Open Source Computer Vision Library (OpenCV) and the Convolutional Neural Networks (CNN), to extract English text from images efficiently.The CNN model is based on a two-stage pipeline that uses a single neural network to directly detect the characters in the scene images.It eliminates the unnecessary intermediate steps that are present in the previous approaches to this task making them slower and inaccurate, thereby improving the time complexity and the performance of the algorithm.
Adaptive model updating for robust object tracking

In this paper, we exploit features extracted from convolutional neural network (CNN) to be better utilized for visual tracking. It is observed that CNN features in higher levels provide semantic information which is robust to appearance variations. Thus we integrate the hierarchical features in different layers of a deep model to correlation filter tracking framework. More specifically, correlation filters are learned on each layer to encode the object appearance. The peak-to-sidelobe ratio (PSR) is employed to measure the differences between image patches. To leverage the robustness of our model, we develop an adaptive model updating scheme to train the correlation filters according to different response maps. Extensive experimental results on three large scale benchmark datasets show that the proposed algorithm performs favorably against state-of-the-art methods.
Incentives for Crypto-Collateralized Digital Assets

Digital currencies such as Bitcoin frequently suffer from high price volatility, limiting their utility as a means of purchasing power. Hence, a popular topic among cryptocurrency researchers is a digital currency design which inherits the decentralization of Bitcoin while somehow mitigating its violent price swings. One such system which attempts to establish a price-stable cryptocurrency is the BitShares market-pegged-asset protocol. In this paper, we present a simple mathematical model of the BitShares protocol, and analyze it theoretically and numerically for incentive effects. In particular, we investigate how the selection of two key design parameters function as incentive mechanisms to encourage token holders to commit their core BitShares tokens as collateral for the creation of new price-stabilized tokens. We show a pair of analytical results characterizing some simple facts regarding the interplay between these design parameters. Furthermore, we demonstrate numerically that in some settings, setting these design parameters is a complex, sensitive, and unintuitive task, prompting further work to more fully understand this design process.
A Symmetry-Breaking Node Equivalence for Pruning the Search Space in Backtracking Algorithms

We introduce a new equivalence on graphs, defined by its symmetry-breaking capability. We first present a framework for various backtracking search algorithms, in which the equivalence is used to prune the search tree. Subsequently, we define the equivalence and an optimization problem with the goal of finding an equivalence partition with the highest pruning potential. We also position the optimization problem into the computational-complexity hierarchy. In particular, we show that the verifier lies between P and NP -complete problems. Striving for a practical usability of the approach, we devise a heuristic method for general graphs and optimal algorithms for trees and cycles.
Music Teacher’s Competences in the 21st Century

Rethinking education of music teachers for new learners’ generation is as crucial as identifying the new competencies that today’s music teachers need to develop. According to analyses of EU documents and scientists’ conceptions concerning music teacher’s competences, the author characterizes tendencies and priorities for improving music teachers’ competences in the 21st century. The basic task of music education is promoting the formation of learners’ personal attitude to the world, to self and others, to provide the opportunity for learners to learn music as a new form of communication. In this context music teacher has to demonstrate knowledge and understanding not only in music subject, but in a) communication and positive relationship-building; b) collaboration with parents, colleagues, musicians, composers and specialists in other spheres; c) development of creative and social skills of learners based on three pedagogical principles – personalization, participation and productivity, as well as through and in the arts/culture approaches in music education process.
Nonlinear fractional diffusion model for deblurring images with textures

It is a long-standing problem to preserve fine scale features such as texture in the process of deblurring. In order to deal with this challenging but imperative issue, we establish a framework of nonlinear fractional diffusion equations, which performs well in deblurring images with textures. In the new model, a fractional gradient is used for regularization of the diffusion process to preserve texture features and a source term with blurring kernel is used for deblurring. This source term ensures that the model can handle various blurring kernels. The relation between the regularization parameter and the deblurring performance is investigated theoretically, which ensures a satisfactory recovery when the blur type is known. Moreover, we derive a digital fractional diffusion filter that lives on images. Experimental results and comparisons show the effectiveness of the proposed model for texture-preserving deblurring.
Accessing the bath information in open quantum systems with the stochastic <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>c</mml:mi></mml:mrow></mml:math> -number Langevin equation method

In traditional open quantum systems, the baths are usually traced out so that only the system information is left in the equations of motion. However, recent studies reveal that using only the system degrees of freedom can be insufficient. In this work, we develop a stochastic $c$-number Langevin equation method which can conveniently access the bath information. In our approach, the studied quantities are the expectation values of operators which can contain both system operators and bath operators. The dynamics of the operators of interest is formally divided into separate system and bath parts, with auxiliary stochastic fields. After solving the independent stochastic dynamics of the system part and the bath part, we can recombine them by taking the average over these stochastic fields to obtain the desired quantities. Several applications of the theory are highlighted, including the pure dephasing model, the spin-boson model, and an optically excited quantum dot coupled to a bath of phonons.
Global Attention Pyramid Network for Semantic Segmentation

Global Attention Pyramid module is a combination of the two pyramid structures and the mechanism of attention. In the paper, we use the two pyramid structures based on the image classification model of deep convolutional neural networks(DCNNs), which taking out the feature maps of different resolutions from the basic image classification model for further processing by attention mechanisms or pyramid pooling structure. The two pyramid structures are used in cooperation with each other. The larger pyramid structure is used to capture the detailed information of the image, while the smaller pyramid structure uses the pyramid pooling module to capture the semantic information of the feature map. Global Attention Pyramid Network(GAPNet) is an end-to-end network without the post-processing of the network such as conditional random fields(CRF). We have obtained excellent results in the Cityscapes datasets. The evaluation metric of results are mean intersection over union(mIoU), and the results of experiment that we got are respectively (67.6/70.7) in test datasets/validation datasets.
DCMIX: Generating Mixed Workloads for the Cloud Data Center

To improve system resource utilization, consolidating multi-tenants’ workloads on the common computing infrastructure is a popular way for the cloud data center. The typical deployment of the modern cloud data center is co-locating online services and offline analytics applications. However, the co-locating deployment inevitably brings workloads’ competitions for system resources, such as the CPU and the memory resources. These competitions result in that the user experience (the request latency) of the online services cannot be guaranteed. More and more efforts try to assure the latency requirements of services as well as the system resource efficiency. Mixing the cloud workloads and quantifying resource competition is one of the prerequisites for solving the problem. We proposed a benchmark suite—DCMIX as the cloud mixed workloads, which covered multiple application fields and different latency requirements. Furthermore the mixture of workloads can be generated by specifying mixed execution sequence in the DCMIX. We also proposed the system entropy metric, which originated from some basic system level performance monitor metrics as the quantitative metric for the disturbance caused by system resource competition. Finally, compared with the Service-Standalone mode (only executing the online service workload), we found that $$99^{th}$$ percentile latency of the service workload under the Mixed mode (workloads mix execution) increased 3.5 times, and the node resource utilization under that mode increased 10 times. This implied that mixed workloads can reflect the mixed deployment scene of cloud data center. Furthermore, the system entropy of mixed deployment mode was 4 times larger than that of the Service-Standalone mode, which implied that the system entropy can reflect the disturbance of the system resource competition. We also found that the isolation mechanism has some efforts for mixed workloads, especially the CPU-affinity mechanism.
Hand Trajectory Extraction of Human Assembly Based on Multi-Leap Motions

For the purpose of extracting the assembly motions of workers in 3C (Computer / Communication / Consumer Electronic) assembly lines and meet the needs of light weight, portability and non-contact measurement, this paper proposed a low-cost 3D motion extraction system using Leap Motions to obtain the six-dimensional (position and orientation) trajectory of the assembly motion of human hands. The multi-sensor layout is designed by analyzing the characteristics of assembly motions. To tackle the occlusion problem of Leap Motion, a multi-sensor data fusion method is presented by which the tracking precision of assembly motion in the range of target visual field is also improved. Finally, the six-dimensional trajectory extraction is verified by a simple assembly task. And the recurrence of the human assembly task is performed by UR5 (Universal Robot 5 Kg) in simulation environment of V-REP (Virtual Robot Experimentation Platform).
An Efficient Real time Product Recommendation using Facial Sentiment Analysis

Image recognition based on deep learning has made successful attempts in solving Computer Vision problems. Deep learning algorithms have primarily contributed to face detection, facial expression recognition, age and gender determination tasks. The performances of the algorithms have been evaluated by the researchers and they are made available as cloud services. By accessing the cloud, application developers can incorporate the already tested algorithms in their work and pay for the utilized service. Amazon Rekognition is a web service which provides highly accurate facial analysis and more suitable for facial recognition. Amazon Rekognition includes a simple, easy-to-use API that can quickly analyze any image file that's stored in Amazon S3. This paper explains the necessary steps to utilize the service to build an application for a retail store. The application captures the customer faces, performs facial analysis and recommends appropriate products by displaying the targeted advertisements. With the facial analysis responses of recognition algorithm, a decision block is devised to select the appropriate content to be displayed. The application is tested with anonymous images and performance is appreciable.
Segmentation of Soft Tissues and Tumors from Biomedical Images using Optimized K-Means Clustering via Level Set formulation

Biomedical Image-segmentation is one of the ways towards removing an area of attentiveness by making various segments of an image.The segmentation of biomedical images is considered as one of the challenging tasks in many clinical applications due to poor illuminations, intensity inhomogeneity and noise.In this paper, we propose a new segmentation method which is called Optimized K-Means Clustering via Level Set Formulation.The proposed method diversified into two stages for efficient segmentation of soft tissues and tumor's from MRI brain Scans Images, which is called pre-processing and post-processing.In the first stage, a hybrid approach is considered as pre-processing is called Optimized K-Means Clustering which is the combined approach of Particle Swarm Optimization (PSO) as well as K-Means Clustering for improve the clustering efficiency.We choose the ‗optimal' cluster centers by Particle Swarm Optimization (PSO) algorithm for improving the clustering efficiency.During the process of pre-processing, these segmentation results suffer from few drawbacks such as outliers, edge and boundary leakage problems.In this regard, post-processing is necessary to minimize the obstacles, so we are implementing pre-processing results by using level-set method for smoothed and accurate segmentation of regions from biomedical images such as MRI brain images over existing level set methods.
On the Angles of Change of the Neighborhood Motion Maps on the Triangular Grid

Rotations of digital images are important geometric transformations which are considered in various digital grids. These digitized rotations have different properties than the analogous Euclidean rotations. On the triangular grid, we consider the digitized rotations of a pixel (referred as the main pixel) together with its closest neighbor pixels. Given a main pixel, of a certain distance dfrom the origin, we calculate the angles of the rotations where the neighborhood motion map of the main pixel changes. The neighborhood motion map changes when the respective locations of the closest neighbors change. We also differentiate the cases when the neighborhood motion map is injective and not injective. The former case is connected to the bijective digital rotations, while the latter case is connected to the cases when some image information is lost (in the neighborhood of the main pixel).
Fast surface profilometry utilizing structured illumination microscopy based on the time-domain phase-shift technique

Structured illumination microscopy (SIM) has attracted much research interest due to its high accuracy, strong adaptability, and high efficiency. Existing SIM is mainly based on the phase-shift technique, Hilbert transform technique, and global Fourier transform technique. The phase-shift technique is most widely applied for its higher accuracy, and both the phase-shift technique and Hilbert transform technique suffer from lower speed because multiple images are needed to obtain modulation information for each scanning step. The global Fourier transform technique has a higher speed, but the high-frequency information of the sample will inevitably be lost because a filter window is used. As a result, the global Fourier transform technique is limited to smooth surfaces. In this paper, a fast surface profilometry using SIM is proposed. It is based on the time-domain phase-shift technique (SIM-TPT), which combines one-fringe projection and phase shift. In this proposed measurement system, vertical scanning of the object is synchronized with the switching of the phase-shifted fringe pattern. As a result, only one fringe pattern must be projected, which enables a point-to-point processing defined as the local Fourier transform method in this paper to be utilized to extract the modulation information that will preserve the high-frequency information of the image so it can be applied to both smooth and rough surfaces. Compared to conventional SIM, SIM-TPT has a higher speed because it is a simpler system and can be applied to complex structures such as high roughness surfaces and steep edges.