Reviewerly/exHarmony huggingface dataset:
Prerequisite Relation Learning for Course Concepts Based on Hyperbolic Deep Representation

With the rapid development of MOOCs, more and more learners participate in online learning to improve their abilities. However, students from different educational backgrounds have different starting points, requirements and foundation. How to use the hierarchical relationship between course concepts to provide learners with personalized learning courses can help learners overcome difficulties and reduce the dropout rate of online learning. course concept has the characteristics of hierarchy and prerequisite relation. We can integrate the prior knowledge of hierarchy into the feature vector of course concept. Traditional methods based on machine learning or deep learning embed the features of course into Euclidean space, thereby losing the embedded feature information of hierarchical relationship between course concepts. In recent years, the deep learning method based on hyperbolic space has unique advantages in representing hierarchical structure data. Based on this, we propose a hyperbolic deep learning representation based course concept relationship prediction algorithm, which can better retain hierarchical relationship information between concepts. First, we use the word embedding method of hyperbolic space to express the course concepts (feature vector extraction), then we has defined the distance of the two vectors of in hyperbolic space and calculate the distance between course concepts, finally we determine whether there is a prerequisite relationship between course concepts through hyperbolic FFNN and hyperbolic MLR.The experimental results show that the hyperbolic space is better than the Euclidean space. (F1 scores improved 6.8%).
A New Model for Arabic Text Clustering by Word Embedding and Arabic Word Net

A major challenge in article clustering is high dimensionality, because this will affect directly to the accuracy.However, it is becoming more important due to the huge textual information available online.In this paper, we proposed an Arabic word net dictionary to extract, select and reduce the features.Additionally, we use the embedding Word2Vector model as feature weighting technique.Finally, for the clustering uses the hierarchy clustering.Our methods are using the Arabic word net dictionary with word embedding, additionally by using the discretization.This method are effective and can enhance improve the accuracy of clustering, which shown in our experimental results.
Conversion from electrocardiosignals to equivalent electrical sources on heart surface

Abstract Background The actual task of electrocardiographic examinations is to increase the reliability of diagnosing the condition of the heart. Within the framework of this task, an important direction is the solution of the inverse problem of electrocardiography, based on the processing of electrocardiographic signals of multichannel cardio leads at known electrode coordinates in these leads (Titomir et al. Noninvasiv electrocardiotopography, 2003), (Macfarlane et al. Comprehensive Electrocardiology, 2nd ed. (Chapter 9), 2011). Results In order to obtain more detailed information about the electrical activity of the heart, we carry out a reconstruction of the distribution of equivalent electrical sources on the heart surface. In this area, we hold reconstruction of the equivalent sources during the cardiac cycle at relatively low hardware cost. ECG maps of electrical potentials on the surface of the torso (TSPM) and electrical sources on the surface of the heart (HSSM) were studied for different times of the cardiac cycle. We carried out a visual and quantitative comparison of these maps in the presence of pathological regions of different localization. For this purpose we used the model of the heart electrical activity, based on cellular automata. Conclusions The model of cellular automata allows us to consider the processes of heart excitation in the presence of pathological regions of various sizes and localization. It is shown, that changes in the distribution of electrical sources on the surface of the epicardium in the presence of pathological areas with disturbances in the conduction of heart excitation are much more noticeable than changes in ECG maps on the torso surface.
ROALD DAHL'S INGENIOUS LANGUAGE PLAY IN THE USE OF FIGURATIVE LANGUAGE

Roald Dahl is widely known for being one of the most creative writers, both in the ideas of the stories and the language use. His use of language is exceptional and tends to show some playfulness which makes the writing even more attractive. This play on language is in fact considered his writing style. What is worth noticing further is that playfulness can also be seen in the use of figurative language. This paper specifically focuses on the figurative language such as similes, metaphors, and personification in Dahls novels for children: The BFG, Charlie and the Great Glass Elevator, and The Witches. Since this paper reveals Dahls use of language play as his writing style, it belongs to Stylistics, the study of style. The paper uses a descriptive qualitative method. The data of similes and personification are collected and then analysed in terms of how they show the ingenious side of the writer in using language. The findings show that the figurative language used in the novels indeed makes optimal use of sound play and wordplay. Besides, the figurative language is also closely associated with the fact that the novels are intended for children.
Dealing With Technological Trajectories: Where We Have Come From and Where We Are Going

Technological progress is widely recognized as having considerable impact on human societies, which have become more and more dependent on human-made tools that entail intricate scientific processes, considered <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">technology</i> [item 1) in the Related Works]. The adoption of such tools is borne out of the necessity of survival at first [item 2) in the Related Works], and subsequently as a means to improve access to food, shelter, and clothing; arguably things that we cannot live without. Nevertheless, the urgency of understanding the role of technology in human life has never been as vital as today. Indeed, one could say that technological progress is the key ingredient driving human civilization: the invention of ever more complex tools traces the history of human civilization and its development. Each generation of technology lays the foundation for the next, the invention of simple machines, such as the wheel, for example, enabled the invention of the wheelbarrow, which enabled in turn building larger structures. In parallel, the wheel enabled the construction of gears to transmit power from crankshaft to driveshaft and also the rotation of magnetic tapes permitting the capture of binary data leading to the spinning hard disk drives and their superior digital data storage capacity.
A coordinated control system for dual-arm mobile manipulator balancing grasping and viewpoint selection

The mobile manipulators have been developed as a new robot platform due to its dexterity and mobility, and the motion planning coordinating manipulation and locomotion has been studied. In this paper, we deal with a mobile robot with dual-manipulators, where the one arm is equipped with a RGB-D camera to capture the image for the grasping targets with another arm with a gripper. A grasping task requires a cooperative control of dual-arms with the camera and gripper as well as positioning of the mobile base of the robot. An appropriate grasping posture has to be determined considering the robot”s viewpoint selection such as frame-out avoidance of the visual target and self-occlusion of the arms avoidance, also the target distance has to be maintained in the measurable range from the depth sensor on the camera. We report the control system where request for the grasping posture such as joint limit avoidance of each arm and collision avoidance of the robot-base, in addition to viewpoint selection, are prioritized and coordinated by Projected Gradient control. The experiment results by consecutive pick and place tasks with the robot validate effectiveness of the proposed coordinated control.
Latency-Insensitive Controller for Convolutional Neural Network Accelerators

A novel latency-insensitive controller architecture is proposed for a convolutional neural network (CNN) accelerator. It is shown that the proposed architecture not only guarantees the correct operation, regardless of memory latency, but also maximizes the performance. The implementation results show that the latency-insensitive controller occupies only a negligible fraction of the total area.
Implementation of A* Algorithm for Solving Sokoban Logic Games

Sokoban is one of the puzzle category games that requires players to push boxes towards the top, bottom, left and right until the specified destination. Sometimes it takes a long time to complete a very difficult level so that a solution is needed. So that these difficulties can be overcome, we need an algorithm that can help find the right path.A * algorithm is an algorithm that minimizes the total cost of the track under the right conditions that will provide the best solution in optimal time. A * algorithm will find the distance of the fastest route to be taken by a starting point to the destination object by removing unnecessary steps.From the test results, this A * algorithm can help players in finding a solution to the road in the form of completion steps in the Sokoban game by determining the smallest F value. Keywords - Artificial Intelligence, Games, Sokoban, A * algorithm
Speech Recognition Based on Deep Tensor Neural Network and Multifactor Feature

This paper presents a speech recognition system based on deep tensor neural network which uses multifactor feature as input feature of acoustic model. First, a deep neural network is trained to estimate articulatory feature from input speech, where the training data is MOCHA database[1]. Mel frequency cepstrum coefficients in conjunction with articulatory feature are used as multifactor feature. Deep tensor neural network which involves tensor interactions among neurons is used as the acoustic model in this system. Speech recognition results indicate that the multifactor feature helps in improving speech recognition performance not only under clean conditions but also under noisy background conditions; deep tensor neural network is more capable of modeling multifactor features because of its tensor interactions than deep neural network.
Demonstration of a flexible scheme for two-qubit quantum computation with single photon

To build a quantum computing device, which is capable of generating arbitrary input states and performing universal unitary gate operations (UUGOs), is an important goal in the field of quantum information science. However, only a few special quantum computations have been reported by now based on specific input states and well-designed information processors. Here, we demonstrate a flexible scheme for two-qubit quantum computations by employing the polarization and the spatial mode of a single photon. Two-qubit UUGOs both in free-space optics and for arbitrary pure input states consisting of separable states and entangled states are presented. Quantum state tomography and process tomography are used to characterize the fidelity of the output states and the gate operations we considered. Beyond a demonstration, we believe that our work also enriches the techniques of bulk-optics for quantum information study and has a broad application for other fundamental research.
Biclustering of medical monitoring data using a nonparametric hierarchical Bayesian model

In longitudinal studies in which a medical device is used to monitor outcome repeatedly and frequently on the same patients over a prespecified duration of time, two clustering goals can arise. One goal is to assess the degree of heterogeneity among patient profiles. A second yet equally important goal unique to such studies is to determine frequency and duration of monitoring sufficient to identify longitudinal changes. Considering these goals jointly would identify clusters of patients who share similar patterns over time and characterize temporal stability within each cluster. We use a biclustering approach, allowing simultaneous clustering of observations at both patient and time levels and using a nonparametric hierarchical Bayesian model. Because clustering units at the time level (i.e., time points) are ordered and hence unexchangeable, we utilize a multivariate Dirichlet process mixture model by specifying a Dirichlet process prior at the patient level whose base measure employs change points at the time level to achieve the desired joint clustering. We consider structured covariance between consecutive time points and assess model performance through simulation studies. We apply the model to data on 24-hr ambulatory blood pressure monitoring and examine the relationship between diastolic blood pressure and pediatric obstructive sleep apnoea.
Applying Neural Network to Determine Patterns in Open Source Software

Nowadays, various design techniques are proposed for implementing different kinds of solution for the software desired for complex systems. Among them design patterns are frequently used to identify a better solution for the object-oriented software. The recognition of design pattern in open source program enables the maintainability of traditional object-oriented programs. The proposed methodology shows the determination of design patterns in open source program. The process of pattern determination is done by using quality measurement parameters e.g., software metrics. The presented method considers various kind of neural network models to determine the presence of design patterns in open source system. The proposed methodology is evaluated by using a case study e.g., JUnit. Subsequently, a comparative analysis is presented to identify the strengths and weaknesses of selected neural network models.
Robust and Blind Multiple Image Watermarking Using CNN and DWT in Video

Digital watermarking was introduced as a result of rapid advancement of networked multimedia systems. It had been developed to enforce copyright technologies for cover of copyright possession. Due to increase in growth of internet users of networks are increasing rapidly. It has been concluded that to minimize distortions and to increase capacity, techniques in frequency domain must be combined with another technique which has high capacity and strong robustness against different types of attacks. In this paper, a robust multiple watermarking which combine Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT)and Convolution Neural Network techniques on selected middle band of the video frames is used. This methodology is considered to be robust blind watermarking because it successfully fulfills the requirement of imperceptibility and provides high robustness against a number of image-processing attacks such as Mean filtering, Median filtering, Gaussian noise, salt and pepper noise, poison noise and rotation attack. The proposed method embeds watermark by decomposing the host image. Convolution neural network calculates the weight factor for each wavelet coefficient. The watermark bits are added to the selected coefficients without any perceptual degradation for host image. The simulation is performed on MATLAB platform. The result analysis is evaluated on PSNR and MSE which is used to define robustness of the watermark that means that the watermark will not be destroyed after intentional or involuntary attacks and can still be used for certification. The analysis of the results was made with different types of attacks concluded that the proposed technique is approximately 14% efficient as compared to existing work.
Advanced Concepts in Google Dialogflow

In previous chapter, we discussed how to create a simple chatbot. In this chapter, we will discuss advanced topics such as dialog context (input and output contexts), follow-up intents, multilingual bots, webhooks, and more. These advanced features will help us to configure complex use cases.
Virtual Forestry Generation: Evaluating Models for Tree Placement in Games

A handful of approaches have been previously proposed to generate procedurally virtual forestry for virtual worlds and computer games, including plant growth models and point distribution methods. However, there has been no evaluation to date which assesses how effective these algorithms are at modelling real-world phenomena. In this paper, we tackle this issue by evaluating three algorithms used in the generation of virtual forests—a randomly uniform point distribution method (control), a plant competition model, and an iterative random point distribution technique. Our results show that a plant competition model generated more believable content when viewed from an aerial perspective. Interestingly, however, we also found that a randomly uniform point distribution method produced forestry which was rated higher in playability and photorealism, when viewed from a first-person perspective. We conclude that the objective of the game designer is important to consider when selecting an algorithm to generate forestry, as the algorithms produce forestry that is perceived differently.
Dataset for WWW landing pages webobject retrieval performance evaluation

This dataset describes data obtained from a multi-day World Wide Web (WWW) measurement campaign distributed internationally across multiple Amazon Web Service (AWS) datacentres. The Chrome web browser was controlled by the Selenium framework to make repetitive requests to several popular websites; the resulting webobjects were captured by a proxy server and details about them stored in the provided SQLite3 databases. A Python script is provided to evaluate the webobjects with respect to their configured as well as their actual expiration times, as part of our more detailed analysis that we provide in [1]. Researchers and practitioners can readily employ this dataset in their own research endeavours with little efforts for avenues of inquiry beyond webobject expiration times we described in [1], as we provide additional information about each webobject and each website visit during the measurement campaign time horizon.
Blood flow anomaly detection via generative adversarial networks: a preliminary study

This work explores a Generative Adversarial Network (GAN) based approach for hemorrhage detection on color Doppler ultrasound images of blood vessels. Given the challenges of collecting hemorrhage data and the inherent pathology variability, we investigate an unsupervised anomaly detection network which learns a manifold of normal blood flow variability and subsequently identifies anomalous flow patterns that fall outside the learned manifold. As an initial, feasibility study, we collected ultrasound color Doppler images of brachial arteries from 11 healthy volunteers. The images were pre-processed to mask out velocities in surrounding tissues and were subsequently cropped, resized, augmented and normalized. The network was trained on 1530 images from 8 healthy volunteers and tested on 70 images from 2 healthy volunteers. In addition, the network was tested on 6 synthetic images generated to simulate blood flow velocity patterns at the site of hemorrhage. Results show significant (p&lt;0.05) differences in anomaly scores of normal arteries and simulated injured arteries. The residual images, or the reconstruction error maps, show promise in localizing anomalies at pixel level.
Cloud Computing Task Scheduling Based on Improved Differential Evolution Algorithm

In this paper, we propose an improved discrete differential evolution algorithm to solve the problem of cloud computing task scheduling, which can greatly improve the computational efficiency of task scheduling in cloud computing. The purpose of this improved algorithm is to obtain the shortest completion time of a total task and get a better load balance. In the process of population initialization, each individual of the population is coded through natural number, and the crossover constant changes with iteration times, which is used to make a balance between global search and local search. After mutation and crossover operation, a new selection mechanism will be adopted to preserve those excellent individuals among mutation individual, target individual and trial individual. Numerical experiment runs in Cloudsim platform (a computer simulation platform used to stimulate cloud computing environment). Our experimental results show that compared with standard DE algorithm, the improved differential evolution algorithm has a better performance on convergence and a good ability to jump out from local optimum, which means the algorithm can get a better scheduling result for cloud computing. In the experiment, we set the numbers of subtasks as 50, 200 and 500 and the completion time is reduced by 0.83s, 10.94s and 84.13s respectively.
Features of application of fundamental knowledge in innovative space: ontological aspect

The analysis of the innovative space with a complex structure and configuration has been conducted by showing that its space-time frame is uncertain due to the high dynamics. The results obtained from the analysis of virtuum as a kind of multidimensional reality, forming a new image of society and, at the same time, generating and combining new knowledge. Knowledge and innovation are shown as the fundamental dominant of the current model of society and as the result this model combines the characteristics of informational, cybercultural, technogenic, virtual, and innovative society. On this basis, the main purpose of the research was implementation of the correlation of innovative creations and new epistemes, which were the foundation of the innovative space. The ontological approach that became the theoretical basis of this research showed that the innovative space is understood as one of the multidimensional sphere of social existence as such, including all actors of innovation. The results of the research confirmed that fundamental knowledge is updating the epistemological resource, at the same time, the mechanisms and methodology of research are constantly changing, technical and technological tools are being modernized and all of these factors influence on innovative space. Besides, innovative space as an artificial environment is correcting social and natural phenomena.
Real-time computer-generated moiré profilometry with adaptive filtering algorithm

A real-time computer-generated moiré profilometry (CGMP) with adaptive filtering algorithm is proposed. In CGMP proposed previously, only one static sinusoidal grating needs to be projected onto the measured object and only one deformed pattern caused by the object needs to be captured, which is ideal for real-time three-dimensional (3-D) measurement. By means of generating moiré fringes between the captured deformed pattern and the prestored fringe patterns on the reference plane via computer, the 3-D shape of the measured object can be reconstructed successfully. In phase calculating, this method needs to extract the DC component by filtering. If filtering operation required manual intervention each time, its real-time measuring feature may be limited. For this, an adaptive filtering algorithm based on the maximum fundamental frequency component proportion evaluation function is proposed. It can automatically retrieve an object’s phase information according to the Fourier spectrum distributions of different measured objects without any manual intervention. Experimental results demonstrate the feasibility and validity of the real-time 3-D measuring application.