Reviewerly/exHarmony huggingface dataset:
A further study of some Markovian Bitcoin models from Göbel et al.

We consider two different continuous-time Markov chain models recently studied in Göbel et al.[8 Göbel, J.; Keeler, H. P.; Krzesinski, A. E.; Taylor, P. G. Bitcoin blockchain dynamics: The selfish-mine strategy in the presence of propagation delay. Perform. Eval. 2016, 104, 23–41. DOI: 10.1016/j.peva.2016.07.001.[Crossref], [Web of Science ®] , [Google Scholar]], which were created to model the interactions between a small pool of miners, and a larger collection of miners, within the Bitcoin network. The first model we discuss represents the case where all miners behave honestly and follow the Bitcoin protocol, while the second model represents the case where the smaller pool of miners use the Selfish Mining strategy of Eyal and Sirer[3 Eyal, I.; Sirer, E. G. Majority is not enough: Bitcoin mining is vulnerable. In Financial Cryptography and Data Security; Springer, Berlin, 2013; 436–454. [Google Scholar]]. We give a new derivation of the stationary distribution of the process in the honest mining case and further build on the results of Göbel et al.[8 Göbel, J.; Keeler, H. P.; Krzesinski, A. E.; Taylor, P. G. Bitcoin blockchain dynamics: The selfish-mine strategy in the presence of propagation delay. Perform. Eval. 2016, 104, 23–41. DOI: 10.1016/j.peva.2016.07.001.[Crossref], [Web of Science ®] , [Google Scholar]] by showing that the normalizing constant can be expressed in closed-form. We also use similar techniques to derive expressions for the Laplace transforms of the transition functions. We then illustrate how these techniques yield similar expressions for the stationary distribution of the process when the smaller pool implements Selfish Mining: the Laplace transforms of the transition functions can be calculated as well. Lastly, we briefly explain how our methods can be extended to more general models of a similar type.
Cognitive, Affective, Social and Cultural Aspects of Teaching and Learning in Media Studies

&lt;p style="text-align:justify"&gt;The vision that we will present is not intended to be exhaustive or totalizing, although it does attempt to highlight the most significant aspects that characterize it. It is a reflection of more than twenty years of work in the educational field, at different levels and with different functions, it is also the result of personal evolution, and how she will be subject to evolution. Comparing media studies with the 19th century, it seems that media has conquered an essential position in every individual's life. This study meets the requirements of a growing pool of traditional and non-traditional students, including teachers, who find education as an important aspect for jobs in the modern age of information and technology. This research will utilize the literature which has been published already, including quantitative as well as qualitative measures. Past papers were selected, which were published in different areas. Comparing with other empirical research, the first phase of the procedure of the study is to collect the data The study is to identify the teachers and learner's characteristics such as affective, cognitive, and social factors which causes an impact on learning and teaching enhanced environment in media studies.&lt;/p&gt;
Generating Natural Adversarial Remote Sensing Images

Over the last years, remote sensing image (RSI) analysis has started resorting to using deep neural networks to solve most of the commonly faced problems, such as detection, land cover classification, or segmentation. As far as critical decision-making can be based upon the results of RSI analysis, it is important to clearly identify and understand potential security threats occurring in those machine learning algorithms. Notably, it has recently been found that neural networks are particularly sensitive to carefully designed attacks, generally crafted given the full knowledge of the considered deep network. In this article, we consider the more realistic but challenging case where one wants to generate such attacks in the case of a black-box neural network. In this case, only the prediction score of the network is accessible, on a specific dataset. Examples that lure away the network’s prediction, while being perceptually similar to real images, are called natural or unrestricted adversarial examples. We present an original method to generate such examples based on a variant of the Wasserstein generative adversarial network. We demonstrate its effectiveness on natural adversarial hyperspectral image generation and image modification for fooling a state-of-the-art detector. Among others, we also conduct a perceptual evaluation with human annotators to better assess the effectiveness of the proposed method. Our code is available for the community: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/PythonOT/ARWGAN</uri> .
NRMVS: Non-Rigid Multi-View Stereo

Multi-view Stereo (MVS) is a common solution in photogrammetry applications for the dense reconstruction of a static scene from images. The static scene assumption, however, limits the general applicability of MVS algorithms, as many day-to-day scenes undergo non-rigid motion, e.g., clothes, faces, or human bodies. In this paper, we open up a new challenging direction: Dense 3D reconstruction of scenes with non-rigid changes observed from a small number of images sparsely captured from different views with a single monocular camera, which we call non-rigid multi-view stereo (NRMVS) problem. We formulate this problem as a joint optimization of deformation and depth estimation, using deformation graphs as the underlying representation. We propose a new sparse 3D to 2D matching technique with a dense patch-match evaluation scheme to estimate the most plausible deformation field satisfying depth and photometric consistency. We show that a dense reconstruction of a scene with non-rigid changes from a few images is possible, and demonstrate that our method can be used to interpolate novel deformed scenes from various combinations of deformation estimates derived from the sparse views.
Resonant Quantum Search with Monitor Qubits*

We present an algorithm for the generalized search problem (searching k marked items among N items) based on a continuous Hamiltonian and exploiting resonance. This resonant algorithm has the same time complexity <?CDATA $O(\sqrt{N/k})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:msqrt> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>/</mml:mo> <mml:mi>k</mml:mi> </mml:mrow> </mml:msqrt> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> as the Grover algorithm. A natural extension of the algorithm, incorporating auxiliary “monitor” qubits, can determine k precisely, if it is unknown. The time complexity of our counting algorithm is <?CDATA $O(\sqrt{N})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:msqrt> <mml:mi>N</mml:mi> </mml:msqrt> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> , similar to the best quantum approximate counting algorithm, or better, given appropriate physical resources.
Point-by-point feature extraction of artificial intelligence images based on the Internet of Things

In the context of the rapid development of artificial intelligence in the Internet of Things, the establishment of the Internet of Things can promote rapid progress in the field of artificial intelligence. Traditional image detection methods use wavelet energy algorithms to divide background and edge noise, with poor resolution and low image detection accuracy. A series of problems such as slow detection speed and lack of image depth analysis exists. Aiming at the disadvantages of traditional methods, this study proposes the design of an artificial intelligence image detection system based on the Internet of Things, and uses intelligent artificial pixel feature collection technology to extract point-by-point feature of the image. This paper introducing artificial intelligence learning algorithms to the wheel detection in the workshop under the Internet of Things system, which can not only solve the problem of poor feature anti-interference and poor robustness in the traditional method, but also has important significance for the secondary development of the wheel detection system. The neural network can be used to classify wheel images while integrating other detection requirements, such as wheel defect detection and wheel number identification. The rich data resources and processing capabilities of the Internet of Things to perform feature analysis and feedback on the collected image pixels are utilized. The artificial intelligence of image synthesis module performs image conversion processing on the signal processes the feedback signal. The analysis results can complete image detection and complete artificial intelligence images. Through simulation experiments, it is proved that the design of artificial intelligence image detection system based on the Internet of Things has the advantages of high image detection rate, high recognition accuracy, stable operation, and efficient processing. The design idea has good application value.
Hotel Content-Based Recommendation System

Abstract Content based recommendation system tries to recommend items similar to those a given user has likely in the past, whereas systems designed according to the collaborative recommendation paradigm identify users whose preferences are similar to those of the given user and recommend items they have liked. The proposed recommendation system is discussing about Data finitihotel recommendations for a place across U.S. The selected process for the recommendation is calculating the rating of hotel categories on the city. By calculating hotel categories from several city by combining two features namely categories and taken from the city of the selected hotelcategory as well. The result of process will be recommended hotel with highest ratingto the user.
Enhancing atom-field interaction in the reduced multiphoton Tavis-Cummings model

We propose a scheme to enhance the light-matter coupling in a cavity QED system where two $\mathrm{\ensuremath{\Lambda}}$ atoms are weakly coupled to a single-mode cavity via multiphoton interaction. By introducing a second-order nonlinear (parametric) driving of the cavity, the original $N$-photon exchanges between the atoms, and the cavity can be reduced to either an effective single- or two-photon transition depending on the odevity of $N$, with the effective atom-field interaction strength being significantly enhanced. This results in an $N$-dependent effective coupling strength on top of the exponential enhancement induced only by the parametric driving. As an example, we demonstrate that our proposal can be exploited for the preparation of high-fidelity steady-state entanglement. The proposed scheme could provide a feasible alternative for diverse applications in quantum technologies.
A Probabilistic Model for Sensor Fusion Using Range-Only Measurements in Multistatic Radar

The implementation of multistatic radar architecture for the detection and tracking of targets engendered the development of numerous sensor data fusion models. This article presents a sensor model based on a probabilistic approach for the data fusion in the multistatic radar. The received polar coordinate measurements are converted to the Cartesian coordinates for the state estimation of the target using a Kalman filter. Then a distributed probabilistic maximum likelihood estimation based distributed data fusion method with five sensors is implemented for the data fusion. The simulation results validate the effectiveness of the data fusion algorithm under various measurement noise conditions. It is observed that the performance of the probabilistic data fusion algorithm performs better as compared with the traditional data fusion algorithm.
Local and Global Graph Approaches to Image Colorization

Image colorization based on numerical modeling gives a highly accurate restoration result when colors are given to enough regions. A lot of numerical models focus on the relation between adjacent pixels; therefore, it is required to give the same color to various regions, and high spatial frequency regions are not colored properly. This letter proposes a colorization algorithm using graph signal processing. The key novelty of our algorithm is a new modeling method for a chrominance image using two different graphs. The first graph is a global graph, which connects the important pixels on an image. The second graph is a local graph, which connects the global graph and each pixel. Based on the hierarchical combination of these two graphs, color image is recovered. Numerical experiments show the effectiveness of the proposed algorithm by comparing with four existing methods.
A Multiple-Search Multi-Start Framework for Metaheuristics for Clustering Problems

Metaheuristic algorithms have been widely used as an effective and efficient way for solving various complex optimization problems; there is, however, plenty of room for improvement.In this research area, the two most important issues that greatly influence the final results of single-solution-based metaheuristic algorithms are in that: (1) some of them are extremely sensitive to the initial solutions, and (2) some of them are easy to fall into a local optimum at early iterations.For these reasons, an effective framework, called multiple-search multi-start for single-solution-based metaheuristic algorithm (MSMS-S), is presented in this paper to mitigate the impact of these issues.MSMS-S ensures that a search procedure will be given different search directions based on the so-called re-start mechanism.To evaluate the performance of the proposed framework, we compare it with several well-known single-solution-based metaheuristic algorithms for clustering and codebook generation problems.Simulation results show that the proposed framework is capable of improving the performance of single-solution-based metaheuristic algorithms.
Competing secure text encryption in intranet using elliptic curve cryptography

AbstractAbstractPrivacy and Security is one of the major requirements of every network. Intranet based applications are used to avoid third party data sharing or prevent sensitive communication from spying or sniffing from Internet service providers. Intranet applications are used to provide safe and secure communication within network and overcome dependency from ISP. It is used by sensitive departments like military, Navy, ISRO and many more. This work attempts to provide secure communication architecture to achieve 'confidentiality and data integrity in more strong way. Secure communication among pear-to-pear devices is important as it accesses remotely in a convenient, controlled and efficient way. For validation and authentication, the cryptographic technique is used. Intranet application connects multiple systems through the network, every organization have their own intranet network. The proposed system implements a secure system using Elliptic curve cryptography for cryptographic algorithm and integrate checksum estimation method. Data is divided into chunks for processing and key is generated for further process. Java technology is used to validate the performance of the proposed model in the context of key generation, encryption, decryption, and checksum calculation.Subject Classification: Primary 93A30Secondary 49K15Keywords: Intranet applicationElliptic curve cryptographyRC6Cryptography techniqueData security
GradMix: Multi-source Transfer across Domains and Tasks

The computer vision community is witnessing an unprecedented rate of new tasks being proposed and addressed, thanks to the deep convolutional networks' capability to find complex mappings from X to Y. The advent of each task often accompanies the release of a large-scale annotated dataset, for supervised training of deep network. However, it is expensive and time-consuming to manually label sufficient amount of training data. Therefore, it is important to develop algorithms that can leverage off-the-shelf labeled dataset to learn useful knowledge for the target task. While previous works mostly focus on transfer learning from a single source, we study multi-source transfer across domains and tasks (MS-DTT), in a semi-supervised setting. We propose GradMix, a model-agnostic method applicable to any model trained with gradient-based learning rule, to transfer knowledge via gradient descent by weighting and mixing the gradients from all sources during training. GradMix follows a meta-learning objective, which assigns layer-wise weights to the source gradients, such that the combined gradient follows the direction that minimize the loss for a small set of samples from the target dataset. In addition, we propose to adaptively adjust the learning rate for each mini-batch based on its importance to the target task, and a pseudo-labeling method to leverage the unlabeled samples in the target domain. We conduct MS-DTT experiments on two tasks: digit recognition and action recognition, and demonstrate the advantageous performance of the proposed method against multiple baselines.
Evolving interval-based representation for multiple classifier fusion

Designing an ensemble of classifiers is one of the popular research topics in machine learning since it can give better results than using each constituent member. Furthermore, the performance of ensemble can be improved using selection or adaptation. In the former, the optimal set of base classifiers, meta-classifier, original features, or meta-data is selected to obtain a better ensemble than using the entire classifiers and features. In the latter, the base classifiers or combining algorithms working on the outputs of the base classifiers are made to adapt to a particular problem. The adaptation here means that the parameters of these algorithms are trained to be optimal for each problem. In this study, we propose a novel evolving combining algorithm using the adaptation approach for the ensemble systems. Instead of using numerical value when computing the representation for each class, we propose to use the interval-based representation for the class. The optimal value of the representation is found through Particle Swarm Optimization. During classification, a test instance is assigned to the class with the interval-based representation that is closest to the base classifiers’ prediction. Experiments conducted on a number of popular dataset confirmed that the proposed method is better than the well-known ensemble systems using Decision Template and Sum Rule as combiner, L2-loss Linear Support Vector Machine, Multiple Layer Neural Network, and the ensemble selection methods based on GA-Meta-data, META-DES, and ACO.
Consultation behaviour with online resources in English-Chinese translation: an eye-tracking, screen-recording and retrospective study

This paper investigates the interaction between translators' perceived translation problems and their online consultation behaviours, and how different consultation behaviours affect translation acceptability. Previous studies indicate that online consultation includes various types of complex information-searching behaviours which, to a great extent, depend on the personal preferences of the web users. In this study, 38 MA translation students translated two 100-word texts from English (L2) into Chinese (L1) using Translog II, with their translations and consultation processes being registered by a Tobii TX300 eye-tracker. The main findings are as follows: (1) an increase in perceived translation difficulty leads to an increase in both the time spent on online consultation and the complexity of the consultation, but does not lead to an increase in the cognitive load expended on consultation; (2) general translation problems, which involve more resource types and longer search times, require more consultation time than specific translation problems; (3) two sub-types of consultation behaviour, information-seeking and information relevance evaluation, are purpose-driven and (4) longer consultation time results in higher acceptability of individual translation solutions, while higher consultation complexity does not.
Mobile and printed dichotomous keys in constructivist learning of biology in primary school

Background: Exploring using constructivist learning has become a focus of educational research in recent years. However, no available research has been found which examines the contribution of constructivist learning through the implementation of handheld computers and tablets (in the literature, a constructivist mobile learning environment refers to constructivist learning with the implementation of mobile devices) to student knowledge across cognitive levels, while their opinion on that contribution in biological education in primary schools is as yet unreported.Purpose: The main aim of this research is to examine the contribution of the Dichotomous Mobile Key (DMK) and the Dichotomous Printed Key (DPK), implemented in the classroom through constructivist learning, to student achievement and the sustainability of the primary school students' biological knowledge about the Systematic and classification of plants at all cognitive levels. The research also analyses students' opinions about the applied dichotomous keys in the constructivist learning process and the correlation between student knowledge and their opinion about the application of the DMK or the DPK.Design and methods: This research uses a quasi-experimental design and involved 140 eight-grade students from Montenegro assigned to two experimental groups, learning through: 1) the DPK (Group C) and 2) the DMK (Group E).Results and conclusions: The results indicate that studying using a DMK is more suitable than a DPK for learning botany in primary schools when implementing a constructivist approach. This is because the DMK is better suited to enhancing some elements of certain constructivist approaches, notably eliciting students' prior knowledge; facilitating exploratory learning; creating cognitive dissonance; and receiving continuous feedback during the entire learning process. The contribution of the DMK is greater than that of the DPK to students' knowledge and opinions to the contribution of the applied dichotomous key to their knowledge and greater desire to learn about plants.
Toward Efficient Image Recognition in Sensor-Based IoT: A Weight Initialization Optimizing Method for CNN Based on RGB Influence Proportion

As the Internet of Things (IoT) is predicted to deal with different problems based on big data, its applications have become increasingly dependent on visual data and deep learning technology, and it is a big challenge to find a suitable method for IoT systems to analyze image data. Traditional deep learning methods have never explicitly taken the color differences of data into account, but from the experience of human vision, colors play differently significant roles in recognizing things. This paper proposes a weight initialization method for deep learning in image recognition problems based on RGB influence proportion, aiming to improve the training process of the learning algorithms. In this paper, we try to extract the RGB proportion and utilize it in the weight initialization process. We conduct several experiments on different datasets to evaluate the effectiveness of our proposal, and it is proven to be effective on small datasets. In addition, as for the access to the RGB influence proportion, we also provide an expedient approach to get the early proportion for the following usage. We assume that the proposed method can be used for IoT sensors to securely analyze complex data in the future.