Reviewerly/exHarmony huggingface dataset:
GHZ states as near-optimal states for reference frame alignment

Let two coordinate systems, in possession of Alice and Bob, be related to each other by an unknown rotation $$R\in \hbox {SO}(3)$$ . Alice is to send identical states $$|\psi _0\rangle $$ to Bob who will make measurements on the received state and will determine the rotation R. The task of Bob is to estimate these parameters of the rotation R by the best possible measurements. Based on the quantum Fisher information, we show that Greenberger–Horne–Zeilinger (GHZ) states are near optimal states for this task. We show concrete measurements which will allow Bob to determine the rotation R. This shows that GHZ states, as superposition of macroscopically distinct states, are useful in yet another context in quantum information, namely in communicating the information of a whole coordinate system between two parties where no prior information is available on the relative orientation of the two frames.
Conditional median-based Bayesian growth mixture modeling for nonnormal data

Growth mixture modeling is a common tool for longitudinal data analysis. One of the key assumptions of traditional growth mixture modeling is that repeated measures within each class are normally distributed. When this normality assumption is violated, traditional growth mixture modeling may provide misleading model estimation results and suffer from nonconvergence. In this article, we propose a robust approach to growth mixture modeling based on conditional medians and use Bayesian methods for model estimation and inferences. A simulation study is conducted to evaluate the performance of this approach. It is found that the new approach has a higher convergence rate and less biased parameter estimation than the traditional growth mixture modeling approach when data are skewed or have outliers. An empirical data analysis is also provided to illustrate how the proposed method can be applied in practice.
Detection of Near-Duplicate Images using Statistical Texture Features

With advancements in digital image processing technology, it is tremendously effortless for anybody to reacquisition high-quality multimedia from an imitating medium. Subsequently, digital image forensics has become progressively significant, in which photographic copying detection is one emerging field. The re-acquisition of digital media results in the introduction of artifacts. The visual quality dropping or blurriness is one of them, which can be effectively extracted using the texture features. State-of-the-art near-duplicate forensic methods are based on either some complex dictionary learning procedure or an improvisational group of features. In contrast to that, we proposed a model based on texture-based statistical features, namely, Multi-scale Local Binary Pattern (MLBP), the Log-Gabor Filter (LoG), and Dual-Tree Complex Wavelet Transform (DTCWT) for classification of near-duplicate or re-acquire images from the singly captured images. The adopted features are second-order statistical texture features. The extracted features perform well on the publicly available datasets, NTU-ROSE, ICL, and Mturk datasets. The comparisons with existing methods exhibit the computational accuracy and efficiency of the proposed model. Features extracted from the dataset of length 32 allows an SVM classifier to achieves an accuracy of 99% for the NTU-ROSE, 96% for ICL and 90% for the Mturk dataset.
Playing Doom with Anticipator-A3C Based Agents Using Deep Reinforcement Learning and the ViZDoom Game-AI Research Platform

The built-in game agents act according to the pre-written scripts and make decisions, take actions like they have been stated. They acquire and take advantage of unfair information, instead of acting flexibly like human players, who make decisions only based on game screens. This chapter focuses on studying the application of Deep Learning and Reinforcement Learning in games agents and the improvement of the related algorithms. The goal is to develop a game agent that makes decisions in human’s way and gets rid of relying on unfair information. A game agent (CNN) is implemented by augmenting the A3C algorithm. This agent takes the original real-time game screen as the input of the network, and then output the matching policy. The agent interacts with ViZDoom and reads the real-time game screen to make decisions for controlling the character to act. This chapter improved the A3C algorithm by adding an anticipator network to the original model structure. The goal of doing this is to make the agent act more like human players. It will generate anticipation before making decisions, then combine the real-time game screen with anticipation images together as a whole input of the network defined by the A3C algorithm. It can use the combination of the data to make decisions and output the discrete actions. Because the method only changes the structure of data for the input of the network, so it is a model-free method and can be easily transplanted to other algorithms. The performance of A3C is compared with variants proposed in this chapter, analyzed the differences between them and gathered the experimental data from the latest articles as a comparison which studies the same problem. The result shows, that the A3C algorithm with Anticipation performs better than the A3C algorithm.
Research on Recommended System Construction Based on SSM Framework

The rapid development of the economy and society is accompanied by the rapid progress of the internet and computer technology. We as consumers are sometimes overwhelmed when faced with so much massive information, not only that small and micro enterprises are facing a similar situation, it is important to find out how to extract useful information from the massive amount of information that can help improve economic efficiency. In this paper, we will construct a recommendation system through the SSM framework based on collaborative filtering recommendation idea, together with Layui and ECharts, in which data visualization can also be done. The management, promotion and warehousing departments of micro and small enterprises can develop more efficient strategic guidelines based on the recommendation results and data visualization displays, thus improving the management of the enterprise and also the economic efficiency of the enterprise at the same time.
Ring-Based Quantum Network with Quantum Key Distribution (QKD) and Quantum Digital Signature (QDS)

Abstract Quantum technological developments offers a great promise for implementing secure quantum internet. In this study, we propose a ring topology-based quantum network with QKD and QDS for the quantum internet. The combination of QKD and QDS allows both the detection of the intruder and the increase in security in the network. The proposed network has been demonstrated to work by being implemented in the SQUANCH network simulator [1].
A Multi-objective Learning Algorithm for Related Recommendations

In this paper, we introduce a novel multi-objective learning algorithm for related recommendations on industrial video sharing platforms. As an indispensable part in recommender system, the related video recommender system faces several realworld challenges, including maintaining high relevance between source item and target items, as well as achieving multiple competing ranking objectives. To solve these, we largely extended model-based collaborative filtering algorithm by adding related candidate generation stage, Two-tower DNN structure and a multi-task learning mechanism. Compared with typical baseline solutions, our proposed algorithm can capture both linear and non-linear relationships from user-item interactions, and live experiments demonstrate that it can significantly advance the state of the art on related recommendation quality.
Continual Learning with Bayesian Model Based on a Fixed Pre-Trained Feature Extractor

Deep learning has shown its human-level performance in various applications. However, current deep learning models are characterised by catastrophic forgetting of old knowledge when learning new classes. This poses a challenge particularly in intelligent diagnosis systems where initially only training data of a limited number of diseases are available. In this case, updating the intelligent system with data of new diseases would inevitably downgrade its performance on previously learned diseases. Inspired by the process of learning new knowledge in human brains, we propose a Bayesian generative model for continual learning built on a fixed pre-trained feature extractor. In this model, knowledge of each old class can be compactly represented by a collection of statistical distributions, e.g. with Gaussian mixture models, and naturally kept from forgetting in continual learning over time. Unlike existing class-incremental learning methods, the proposed approach is not sensitive to the continual learning process and can be additionally well applied to the data-incremental learning scenario. Experiments on multiple medical and natural image classification tasks showed that the proposed approach outperforms state-of-the-art approaches which even keep some images of old classes during continual learning of new classes.
On Biclique Connectivity in Bipartite Graphs and Recommendation Systems

Bipartite graphs can be used to model many real-world relationships, with applications in many domains such as medicine and social networks. We present an application of maximal bicliques of bipartite graphs to recommendation systems that makes use of the notion of biclique similarity of a set of vertices in order to recommend items to users in a certain order of preference. Experimental results using real-world datasets that justify our approach are presented.
Optical Flow Networks for Heartbeat Estimation in 4D Ultrasound Images

Congenital heart defects is one of the most common neonatal diseases and has a very low survival rate. The fetal heart is generally smaller and possesses a faster than normal beating rate, thus making medical diagnosis difficult. The efficiency and accuracy of diagnosis of congenital heart disease can be improved by computer-aided diagnostic methods. Optical flow is a robust algorithm for object recognition and motion detection, and has potential in early detection of congenital heart defects. In this paper, an end-to-end deep learning system is proposed for obtaining the optical flow information from 4D fetal cardiac ultrasound images. The optical flow network model is trained by using gradients of image sequences obtained from a virtual data set. Subsequently, the trained model is used to detect the cardiac motion. Experimental results and performance evaluation demonstrate the effectiveness of the proposed network. Apart from the efficacy of the proposed method, a visualization of the fetal cardiac motion using pseudo-color is provided. It is envisaged that the proposed method can be used in clinical applications requiring automatic detection of congenital fetal heart defects.
On the Probability and Automatic Search of Rotational-XOR Cryptanalysis on ARX Ciphers

Abstract Rotational-XOR cryptanalysis is a very recent technique for ARX ciphers. In this paper, the probability propagation formula of RX-cryptanalysis in modular addition is extended, and the calculation of RX-difference probability for any rotation parameter ($0&amp;lt;k&amp;lt;n$) can be realized. By proposing a concept of RX-offset and constructing the corresponding distribution table, the propagation of RX-difference in modular addition can be derived from the propagation of XOR-difference. Combined with the improvement of the automatic search tool for XOR-differential characteristics of ARX ciphers, we only need to add one more operation in each round, i.e. traverse the possible value of RX-offset and XOR it with the output XOR-difference of modular addition, thus it can achieve the search for RX-differential characteristics. With this method, the RX-differential distinguisher of ARX-C primitives without or with linear key schedule can be searched. For the applications, we have obtained the third-party RX-cryptanalysis results for Alzette and CHAM for the first time as far as we know.
Streaming SPHINCS+ for Embedded Devices Using the Example of TPMs

We present an implementation of the hash-based post-quantum signature scheme SPHINCS $$^+$$ that enables heavily memory-restricted devices to sign messages by streaming-out a signature during its computation and to verify messages by streaming-in a signature. We demonstrate our implementation in the context of Trusted Platform Modules (TPMs) by proposing a SPHINCS $$^+$$ integration and a streaming extension for the TPM specification. We evaluate the overhead of our signature-streaming approach for a stand-alone SPHINCS $$^+$$ implementation and for its integration in a proof-of-concept TPM with the proposed streaming extension running on an ARM Cortex-M4 platform. Our streaming interface greatly reduces the memory requirements without introducing a performance penalty. This is achieved not only by removing the need to store an entire signature but also by reducing the stack requirements of the key generation, sign, and verify operations. Therefore, our streaming interface enables small embedded devices that do not have sufficient memory to store an entire SPHINCS $$^+$$ signature or that previously were only able to use a parameter set that results in smaller signatures to sign and verify messages using all SPHINCS $$^+$$ variants.
Intelligent Question Answering System Based on Entrepreneurial Incubation Knowledge Graph

With the development of science and technology, the importance of innovation for the development of science and technology has become more and more important. In the current era of information explosion, in order to meet the needs of existing enterprises and individuals for obtaining entrepreneurial information, this system has designed an intelligent question-and-answer system based on the entrepreneurial incubation knowledge graph. The system locates the field of innovation, uses crawler software to crawl data in the field of entrepreneurial incubation to construct a knowledge graph, and uses natural language to conduct man-machine dialogue to query and solve related problems of entrepreneurial incubation. The system stores massive amounts of data in the Neo4j graph database, and uses a pattern matching algorithm to connect the natural language input by the user with the knowledge base entity to classify and query the problem. This system has special practical value in the intelligent question answering system of the knowledge graph in the field of entrepreneurial incubation.
Learning from Imbalanced Data Using Over-Sampling and the Firefly Algorithm

In this paper, we consider the problem of learning from imbalanced data. This is one of the main challenges faced by machine learning and classification algorithms. An over-sampling approach is proposed to eliminate the negative influence of a lack of balanced class distribution within a dataset. Synthetic instances are produced within a subset of instances belonging to a minority class, and are generated using so-called neighbours, the selection of which is carried out using the firefly algorithm. The main contribution of the paper is to present an implementation of the firefly algorithm for the production of synthetic instances. The proposed approach is validated using selected benchmark datasets. The results of computational experiments are presented and discussed, and it is shown that the problem of neighbour selection is important with respect to the production quality of synthetic instances, and that the quality can be guaranteed using a dedicated implementation of a metaheuristic approach, i.e. the firefly algorithm. Another contribution of this paper is applying the firefly algorithm to solve a discrete optimisation problem.
Generalized Named Entity Recognition Framework

The process of digitization of data through Optical Character Recognition has been most commonly done through means of Computer Vision. An attempt was made to replace this with a Natural Language Processing method called Named Entity Recognition for extraction of the required data values from the OCR output of the images of documents through means of spaCy. Named Entity Recognition is a technique used to automatically identify named entities in a text and classify them into predefined categories. It helps businesses easily analyze huge amounts of unstructured data. Text Annotation helps machines recognize the crucial words in a sentence making them more powerful. It is an essential requirement for making a dataset that can be used for NLP Models. Labeling the keywords in each statement is important to make the entire statement understandable to machine learning models. A generalized NER framework was developed by the authors which lets users build training models on top of the existing spaCy models to allow for named entity recognition on their text data. The framework takes a configuration file which contains model name, model size and hyperparameters, along with annotated data in JSON format as input, and returns a customized spaCy model as output. An annotation tool STAT was also built by the authors specifically for use with the framework which produced the required training data files. The model built by the authors is based on test configurations with drop as 0.2 and 30 iterations whose results are discussed in the Results Section.
A Weight-adaptive Algorithm of Multi Feature Fusion based on Kernel Correlation Filtering for Target Tracking

In most correlation filter target tracking algorithms, poor accuracy in the tracking process for complex field images of the target and scale change problems. To address these issues, this paper proposes an algorithm of adaptive multi-feature fusion with scale change correlation filtering tracking. Our algorithm is based on the rapid and simple Kernel-Correlated Filtering(K CF) tracker, and achieves the complementarity among image features by fusing multiple features of Color Nmae(CN), Histogram of Oriented Gradient(HOG) and Local Binary Pattern(LBP) with weights adjusted by visual evaluation functions. The proposed algorithm introduces scale pooling and bilinear interpolation to adjust the target template size. Experiments on the OTB-2015 dataset of 100 video frames are compared with several trackers, and the precision and success ratio of our algorithm on complex scene tracking problems are 17.7% and 32.1 % respectively compared to the based-KCF.
Package Management System in Linux

In the Linux environment, there are two common methods for delivering software: source code and pre-compiled binaries. Open-source software can benefit from source-code availability. Close-source software, on the other hand, requires a pre-compiled binary. Unlike Windows, Linux distributions such as Ubuntu, Red Hat, CentOS, and Debian come in a variety of flavours. Various Linux distributions necessitate the use of distinct binary installation packages. Red Hat and CentOS, for example, use. rpm packages, but Ubuntu and Debian require. deb packages. Developers must compile software on as many Linux computers as their supported Linux versions in order to create a software package appropriate for a wide range of Linux distributions. This procedure is inefficient, time-consuming, and difficult to manage. A method for creating and managing software packages is created here, that only requires one Linux machine. This system automates the export of software source code from a version control system, the editing of package configuration, the building of software packages, and the management of software versions. The packages for CPU performance libraries are built using this approach, which has been tested and utilised on a large scale. It has been found to speed up software release, usage and reduce the efforts and complexities. It's also simple to use, especially for those who aren't familiar with Linux.
Design of An End-to-End Autoencoder for Maritime Communication System towards Internet of Vessels

The Internet of Vessels has drawn more and more attention because the shipping efficiency and shipping safety can be increased, and the development of world transportation can easily be augment. Deep learning (DL) has been recognized as very promising for solving bottlenecks in the evolution of next generation communication systems. To adapt to the particularity of the marine environment, Rician fading channel is selected as the channel model for maritime communication. We propose a Convolutional Neural Network based end-to-end Autoencoder (CNN-AE) for the maritime communication system. A 1-dimensional convolution layers is introduced in AE. We investigate the characteristics of the maritime channel and optimize performance by redesigning the parameters. Compared with the existing schemes, the proposed CNN-AE maritime communication system not only inherits the characteristics of traditional AE system which can optimize all modules jointly, but also has the merits of CNN's local connection which can support more information. Simulation results show that under the same condition of input bit lengths and coding rates, when the values of Ricean K-factor increase, the Block Error Rates (BLER) of the system decrease. When the burst noise is added, the BLER grows as the noise increases. When the Ricean K-factor is the same, the BLER decreases as the coding rate decreases.
Improved Nature-Inspired Algorithms in Cloud Computing for Load Balancing

A core area of research is cloud computing. The crucial part of nature-inspired algorithms (NIAs) is now done in the cloud. NIAs are algorithms whose name means that nature influences them. NIAs can further be categorized as swarm intelligence (SI) algorithms, biological phenomena, physics and chemistry processes, or based on any other topic. Si-based algorithms are known as smart since they are known to understand and improve their productivity by evaluating the previous production and the movements that they made. For several real-world optimization challenges that are categorized as NP hard issues, NIAs provide an efficient solution. The NIAs have a long number of implementations, and most of them tend to be more powerful and, therefore, more effective than other algorithms. A great deal of time was spent to make it possible in parallel with other algorithms. Boost output and thus offer a stronger QoS. The paper aims to study different NIAs, and various NIAs are defined to improve for better load balancing in a cloud environment. The principle behind each algorithm is to encourage more study. The paper also mentions numerous NIAs.
Operational Safe Control for Reinforcement-Learning-Based Robot Autonomy

Reinforcement learning (RL) has been widely used for robot autonomy because it can adapt to dynamic or unknown environments by automatically learning optimal control policies from the interactions between robots and environments. However, the practical deployment of RL can endanger the safety of both robots and environments because many RL methods must experience failures during the training phase. These failures can be reduced or avoided by assuming knowing prior knowledge about the states and environments in the training phase, but this assumption is easily invalid in practical applications, especially with unknown environments. In addition, restarting a training episode could be difficult in practice because the robot may be stuck in the failures. To solve these problems, we propose an operational safe control framework that can automatically recover from failures and reduce failure risks without any prior knowledge. Our framework consists of three steps: (1) detect failures and revert to safe actions, (2) collect correction samples to learn a potential that provides internal environment information to robots, (3) use the potential to shape a safe reward that biases safe explorations. A maze navigation example is used to demonstrate that our method outperforms the traditional reinforcement learning with significantly less failures.