Reviewerly/exHarmony huggingface dataset:
Study of correlated Markov noise channels and its effect on quantum speed limit

The time required for a system to change from initial to orthogonal state is known as Quantum speed limit time (QSLT). It has been used to quantify the speed of evolution of quantum systems. Quantum speed limit time is inverse of the quantum evolution. We have investigated the effect of QSLT for corelated Markov noise channels. It has been observed that QSLT under noise channels increases or decreases for a fixed interval of dissipation function. In our work, we consider a two-qubit atomic model for a global system-bath interaction in presence of Markov noise. The evolution of QSLT has been studied under such interactions and its physical significance is also discussed. We hope our new findings, regarding utility of such speed limit will involve research in areas like communication, quantum computation, computation speed in quantum communication, precession limits in quantum metrology.
Legal Documents Links Extraction

The presence of links in a text always causes difficulties in reading. It happens because it is often not easy to follow a text link, what especially comes to legal references. And there are still no solutions published for automate links extraction regarding to Russian legal documents. Therefore, the purpose of this work was to build a method of automatic search, extraction and structure allocation of legal references using a formal grammar. Besides the method we also build a link describing manifest and dataset for evaluation. Moreover, during the work there has been made a research showing how strict are government recommendations to the links writing observed in real life. Evaluation of our method showed that it helps to extract 94% of links with 98% quality.
Preliminaries—Evolutionary Algorithms

In Chap. 2, we presented, based on a general overview of the system identification literature, a set of desired features for the identification framework conceptualized in this thesis. To aid the development of the conceptualized framework, we must first introduce several concepts that will be ultimately fused together to realize the research objective stated in Chap. 1.
Deep Learning

Chapter 3 deals with the deep learning based on the viewpoint that it is an extended version of the feedforward neural network. Section 3.1 describes what is common and what is different between the deep learning and the feedforward neural network. Techniques for pretraining are described in Sects. 3.2 and 3.3.
Extraction and Analysis of Tennis Video Target Slow Motion

Sport, as a healthy and fun form of activity, is practiced by different groups of people. In order to meet the audience's psychological needs, sports video directors will be wonderful shots in the form of slow motion, the scene reproduction. Slow motion contains rich semantic information, so the analysis of slow motion is of great value. In this article, using the existing identity transformation of tennis video as study material, clearly appear in the process of the detection threshold selection of situation, put forward the objective of slow motion tennis video adaptive threshold detection method, the slow motion of adaptive threshold selection problem is resolved, the accuracy and high boundary location accuracy slow motion video frames, this discovery also apply to other sports video in slow motion detection, finally summarized based on the background subtraction and remember a moving target extraction method based on background compensation.
A Multi-modal Males Face Database and Application in Research

This paper presents a face database composed mainly from male faces and testing of some image processing algorithms on the database. The face database takes into account the variation in illumination conditions and beard style which include mustache, beard and face without beard. The database could provide with training and test set to researches related to face recognition and occlusion detection or removing algorithms. Using the database as test set and training set, feature points detection algorithm based on traditional AAM model was tested and similarity of bearded face and normal face images were checked using Perceptual Hashing algorithm to evaluate effectiveness of those algorithms with our database.
Sentence Generation from Conceptual Graph Using Deep Learning

Deep learning (DL) is increasingly used in different fields of research in several domains, among which is Natural Language Generation (NLG). The objective of this work is to study the feasibility of using DL for sentence generation from a Knowledge Representation Formalism, namely Conceptual Graphs (CG). As a first step, we realized an extensive bibliographic work to identify the different aspects and uses of NLG that could be related to our work. Then we trained a sequence-to-sequence model, using a corpus made up of sentences paired with conceptual graphs, that aims to generate sentences from new conceptual graph inputs. The results were evaluated using Bilingual Evaluation Understudy (BLEU) scoring a high value of 0.96.
Modelling via Convex Structures

This chapter presents a variety of nonlinear models related to the Takagi–Sugeno form (TS): some of them are directly inspired from the fuzzy approach in which they first appeared; others are generalisations or derivative structures of the original one. Their common characteristic is the presence of convex structures of the state, time, or exogenous parameters. Since this book is focussed on model-based approaches, different methods are presented to construct the convex models from a given nonlinear one.
Appraisal on Live Virtual Machine Migrations and Its Approaches

Today’s world is Internet world. Almost all the people use internet for accessing different services. In Cloud Computing various cloud consumers demand variety of services as per their dynamically changing needs over the internet. So, it is the job of cloud computing to avail all the demanded services to the cloud consumers. But due to the availability of finite resources it is very difficult for cloud providers to provide all the demanded services in time. From the cloud providers’ perspective cloud resources must be allocated in a fair manner. So, it’s a vibrant issue to meet cloud consumers’ QoS requirements and satisfaction. Virtualization mainly abstracts the resources like CPU and Memory through Virtual Machine for efficient resource utilization. Virtual Machine Migration is one of the key techniques for dynamic resource management in cloud computing. Migration is the process of transferring Virtual Machine from one physical host to another physical host. &nbsp;This paper mainly addresses key performance issues, challenges and techniques for live virtual machine migration in cloud computing. It also focuses on the key issues related to these existing live virtual machine migration techniques and summarizes them.
Introduction

We can learn a lot by observing nature. There is no waste with it. Everything is done in the most economical, optimal way. Particularly impressive is the collective intelligence of a group of individuals working together. Bees, ant colonies, bird flocks, fish passages, etc. can be given as examples of group intelligence. Animals that do not have a high level of individual intelligence deal with difficult problems using a collective approach. This gaves scientists the idea to create algorithms inspired by nature, mimicking the collective intelligence of some animals. These are the so-called metaheuristic methods.
Some properties of maximal trace measure of quantum computer error rate

Interaction of quantum system with environment makes the system change its coherent dynamics and it is extremely important to estimate this deviation in correct and clear way. In this paper we consider measure of decoherence based on trace norm. This measure is defined as a maximal norm of the density matrix deviation. We establish the property of additivity at short times: the sum of the individual qubit error measures provides an estimate of the error for a multiqubit system. This property is investigated for 2-qubit systems numerically. Rigorous analytical proof of asymptotic additivity is obtained.
Auction System for Agricultural Trade Using Blockchain Technology: A Survey with Proposed Framework

Smart approaches such as Artificial Intelligence and the Internet of Things are proven to be superior answers to the expansion of agriculture in terms of efficiency and safety of trade and supply. Agri-trade involves the buying and selling of products that have been produced through farming industries. Online auction is defined as a process where the buyers and sellers can come on an integrated platform and are allowed to bid in a fixed time frame. Decentralized Blockchain-based e-auction system increases auction security and efficiency and transforms the auction process by eliminating intermediaries. With centralized auction, where websites or platforms gather bids and calculate auction outcomes, it is difficult to build confidence among sellers, buyers, and auctioneers. This system lacks transparency, anonymity, and hence prove to be untrustworthy. So, a broad architecture for decentralized auctions that are used to track and trace bids to execute the transactions safely and securely between the seller and the buyers without the involvement of untrusted third parties, is needed. In this work, we give a complete overview of auction systems using blockchain technology. Then the work uses solidity-based smart contracts deployed on Ethereum test network for executing decentralized, privacy preserving auction system. This system enhances the efficiency and safety of trade and supply of agricultural products and is referred to as an Agro auction system using Blockchain. This work opens opportunities for future research to build a complete end-to-end solution for agricultural auction.
A Bytecode Service Composition Engine for Embedded Services

With the development of embedded systems, people tend to abstract the capacity of an embedded equipment as a service in order to simplify the development, deployment, management and maintenance of the embedded software. By aggregating abilities of individual embedded devices via service composition, people can easily build a more reliable and efficient system. Although the service composition problem has been extensively studied in the field of Web, the adoptation of service composition technique to embedded devices stumps due to the resource limitation and platform heterogeneity of embedded systems. To address these problems, this paper builds a service composition engine for embedded systems that comprises three main works: First, this paper provides a uniform method to represent the embedded service composition problem. Second, this paper designs a compiling method based on topological sorting to convert the unified composition information into the service composition file that represents the way to implement the composite service. Third, this paper devises a bytecode virtual machine to execute the service composition file, and implements the composite service in a resource-friendly way. At last, a carefully devised experiment is conducted, and the result shows our devised engine provides a lightweight, reliable and well-performed way to realize the service composition technique on embedded devices.
PS-QMix: A Parallel Learning Framework for Q-Mix Using Parameter Server

With the development of deep reinforcement learning and multi-agent modeling, Multi-Agent Reinforcement Learning (MARL) has become a very active research topic recently. Q-Mix is a popular algorithm for solving MARL tasks where the individual agents are allowed to be trained in a centralized manner. As the scale and complexity of MARL tasks grow, there is an urging requirement for a more efficient training strategy. As a consequence, it is demanding to develop a Q-Mix training algorithm which can benefit from parallel computation. However, how classic distributed machine learning frameworks work with Q-Mix is a less studied problem. In this paper, we propose the PS-Qmix algorithm to apply the Parameter Server framework to training QMix agents in parallel. Our algorithm employs multiple distributed worker threads for data generation and model learning, where these two processes are decoupled and executed in alternation. To cater for different simulation speed of the environment, the proposed algorithm allows the user to tune the relative proportion of computation allocated to data generation and model learning. We evaluate the PS-Qmix algorithm on a StarCraft II micro-combat task. As we increase the number of worker threads, we observe significant speed-up in both data generation and model learning. The evaluation results indicate that our method is effective in utilizing distributed computation resources to train Q-Mix agents.
Adaptive Self-Supervised Graph Representation Learning

Graph neural networks (GNNs) is widely used to learn a powerful representation of graph-structured data. Recently, many works shows that transferring knowledge from self-supervised tasks to downstream tasks can improve graph representation. However, in terms of optimization target and training data, there is a fundamental difference between self-supervised and downstream tasks. The reason is that these methods do not make any adaptations for downstream activities, traditional pre-training approaches may not be successful enough for knowledge transfer. To address these issues, we present a generic learning framework that identifies the solution in graph self-supervised learning where the primary task, rather than auxiliary tasks, obtains the highest performance. Extensive experiments on a variety of datasets show that the proposed framework may efficiently integrate auxiliary tasks with the main task and enhance performance when compared to state-of-the-art methods.
A Task Schedule Approach for Heterogeneous Storm Based on Particle Swarm-Load Balancing Algorithm

Storm is used for the calculation of streaming big data. However, the default task scheduling strategy does not consider the resource situation of the cluster for polling allocation. This paper presents a MDFDRPSO-EDLB algorithm, considers heterogeneous clusters of resource utilization, network factors to the task static scheduling. Using the storm resource benchmark test, compared with the default scheduling strategy, the sum of the node's cpu and memory utilization can be steadily improved.
A Real Time Video Stabilizer Based on Feature Trajectories and Global Mesh Warping

Video recording has become one of the key features of digital cameras nowadays. Though the resolution and quality of frames are the key specs, the stability issue is less addressed. The amount of data involved in video stabilization makes memory bandwidth a bottleneck. Hence existing hardware accelerators are usually designed using a simple approach of displacement compensation, which has limited the performance. In this work, we design a special processor capable of providing a better stabilization function in real time. The hardware techniques implemented include pipelining, parallel processing, memory interleaving, and data ordering. The chip is synthesized with TSMC 40nm technology. An 11X speedup can be achieved when compared to the software version.
Adversarial Training Leaded Robust MRC Method

Machine Reading Comprehension (MRC) is one of the basic tasks in Natural Language Processing, which can be used to test the ability of natural language comprehension of a model. Several models and methods have succeeded in MRC, but they are seldom robust enough, which makes them work ill in the real-world question and answering tasks filled up with noises and confusions. To tackle with that, we propose a model based on pretrained Chinese-MacBERT-Large. In order to get enough robustness, we apply two adversarial methods, i.e. FGM & VAT in training process. The experiments on DuReader_robust show that our model outperforms the baseline based on Chinese-MacBERT-Base without adversarial methods, which means our model is more stable to noises and confusions.
Machine learning for knowledge discovery with R: Methodologies for modeling, inference and prediction, Kao‐TaiTsai, Boca Raton, FL: Chapman and Hall/CRC Press.2021. 244 pages. Hardback price GBP 74.99. ISBN 9781032065366.

Biometrical JournalVolume 64, Issue 4 p. 822-823 BOOK REVIEW Machine learning for knowledge discovery with R: Methodologies for modeling, inference and prediction, Kao-Tai Tsai, Boca Raton, FL: Chapman and Hall/CRC Press. 2021. 244 pages. Hardback price GBP 74.99. ISBN 9781032065366. Roman Hornung, Roman Hornung Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie, Ludwig-Maximilians-Universität München, Munich, GermanySearch for more papers by this authorAnne-Laure Boulesteix, Corresponding Author Anne-Laure Boulesteix [email protected] orcid.org/0000-0002-2729-0947 Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie, Ludwig-Maximilians-Universität München, Munich, Germany Email: [email protected]Search for more papers by this author Roman Hornung, Roman Hornung Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie, Ludwig-Maximilians-Universität München, Munich, GermanySearch for more papers by this authorAnne-Laure Boulesteix, Corresponding Author Anne-Laure Boulesteix [email protected] orcid.org/0000-0002-2729-0947 Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie, Ludwig-Maximilians-Universität München, Munich, Germany Email: [email protected]Search for more papers by this author First published: 05 February 2022 https://doi.org/10.1002/bimj.202200007Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume64, Issue4April 2022Pages 822-823 RelatedInformation
Elliptic Curve Cryptography Algorithm Against Energy Attack

Having high safety and effective computational property, the elliptic curve cryptosystem is very suitable for embedded mobile environment with resource constraints. Energy attack is a powerful cipher attack method, it uses leaking information of cipher-chip in its operation process to attack chip cryptographic algorithms. In view of the situation that the energy attack on the elliptic curve cryptosystem mainly concentrates on scalar multiplication operation an improved algorithm FWNAF based on RWNAF is proposed. This algorithm utilizes the fragments window technology further improves the utilization ratio of the storage resource and reduces the “jitter phenomenon” in system computing performance caused by the sharp change in system resources.
A Study on the Bible Study Model for the Consistency of Faith and Life Using the Story

Due to COVID-19, the church is facing various criticisms in the world. The reason is that it has been pointed out as the epicenter of the spread of COVID-19, but it has been quite a while since the church has already lost trust. The fundamental reason for this phenomenon lies in the loss of the church’s identity as a model for the kingdom of God and the inability of Christians to bear the mission of light and salt in the world.BR In order to restore the essence of the church and fulfill the Christian mission, it is necessary to educate laymen living in the world to match their faith and life. Only properly trained laymen can make the church healthy and realise the gospel of the kingdom of God that Jesus Christ spread in the world.BR It is not that there was no educational opportunity for laymen. However, existing Bible studies that train laymen tend to be separated from life in the form of injecting doctrines. Now, an educational search is required to promote the right establishment of faith and the unity of faith and life. To this end, finding a Bible study model that can connect biblical messages and the lives of laymen can be one way.BR In particular, stories have the power to lead learners to faith. Stories help learners pursue and interpret meaning in events experienced by individuals or communities. Finally, stories help learners experience other people, different races, and cultures in various ways, discovering the universal experience and truth of the human community contained in diversity. Therefore, stories are very useful as a method of biblical learning that can help learners match faith and life by connecting life and Bible.BR Ecumenical biblical stories, Thomas Groome’s shared Praxis, and Bibliodramas can be presented as a Bible study model using stories. These can contribute to the Bible study model that helps laymen fulfill their mission in the world through the consistency of faith and life.
Analysis of Intelligent Energy Saving Strategy of 4G/5G Network Based on FP-Tree

With the large-scale deployment of 5G network of communication operators, there are more and more 5G devices, and the power consumption of mobile network surges. This paper uses the improved FP-Tree (Frequent Pattern Tree) to mine association rules for the service load of 4G/5G base stations of operators. It reduced the number of times of traversing the data set when generating candidate frequent and complex itemsets, and fully considers the problem of low utilization of equipment resources caused by ”Peak Valley Efect”, so as to improve the accuracy and operation efciency of the model. It greatly improves the energy-saving potential of the equipment. The evaluation of the operator’s 4G/5G network KPI, KQI and other indicators shows that the network performance is stable and the user perception is basically unchanged.
Mirror invariant convolutional neural networks for image classification

Deep convolutional neural networks (DCNNs) have been developed rapidly and they perform well on both classification and object detection tasks. However, its strong performance makes people ignore to study the invariance of the DCNNs, such as mirror invariance. In fact, the ability of DCNNs in handling mirror-symmetrical images remains limited. In this paper, a mirror transformation convolutional layer is proposed, which transforms several feature maps to produce mirror-symmetrical feature maps based on the traditional convolutional layer. By combining with the mirror transformation convolutional layer, the DCNNs will have mirror invariance and the performance of neural networks can be improved on classification tasks. A dataset for driver's and passenger's seatbelt detection has been collected, which is used to verify the effectiveness of the proposed convolutional layer. In the experiments, one of the state-of-the-art DCNNs, GoogLeNet, is collaborated with the mirror transformation convolutional layer to form a mirror invariant networks (MINets). The experimental results show that the MINets can achieve better classification performance than the original GoogLeNet. MINets can also reduce the risk of over-fitting caused by applying data augmentations to the dataset.
Aviation safety risk assessment based on fuzzy cognitive map and grey relational analysis

In order to solve the problem of mutual influence of aviation safety risk evaluation indexes, an evaluation method based on fuzzy cognitive map (FCM) and grey relational analysis (GRA) (FCM-GRA) is adopted. The method takes GRA as the main process of evaluation, uses fuzzy cognitive map to express the influence relationship between indicators, realizes the index weighting, and determines the optimal scheme ranking by calculating the grey correlation degree. This method makes up for the shortcoming that GRA can not represent the interaction between evaluation indexes alone. Finally, the method is used to rank the aviation safety risk of each unit, which provides some decision-making reference for the higher authorities to guide the aviation safety risk of each unit.