Reviewerly/exHarmony huggingface dataset:
Fuzzy discretization and control for non-linear, multiple binary input systems

The control of continuous-time linear systems with binary inputs cannot benefit from existing control design techniques because they are based on continuous control actions. In particular, optimal control problems with binary inputs lead to combinatorial optimization problems, which are difficult to solve. In this article we provide an exact discretization model of the binary continuous-time system that results in a non-linear multiple input controlled system. The non-linear model is then converted into a fuzzy discrete Takagi-Sugeno model, thus allowing the use of optimal control techniques based on LMI design. The modelling of the non-linear model by a discrete Takagi-Sugeno model is a complex process but it can be automatically performed as shown in the article and the code of the application examples.
Minimization of the estimation error for entanglement distribution networks with arbitrary noise

Fidelity estimation is essential for the quality control of entanglement distribution networks. Because measurements collapse quantum states, we consider a setup in which nodes randomly sample a subset of the entangled qubit pairs to measure and then estimate the average fidelity of the unsampled pairs conditioned on the measurement outcome. The proposed estimation protocol achieves the lowest mean-square estimation error in a difficult scenario with arbitrary noise and no prior information. Moreover, this protocol is implementation friendly because it only performs local Pauli operators according to a predefined sequence. Numerical studies show that compared to existing fidelity estimation protocols, the proposed protocol reduces the estimation error in both scenarios with independent and identically distributed noise and correlated noise.
Transfer Learning across Graph Convolutional Networks: Methods, Theory, and Applications

Graph neural networks have been widely used for learning representations of nodes for many downstream tasks on graph data. Existing models were designed for the nodes on a single graph, which would not be able to utilize information across multiple graphs. The real world does have multiple graphs where the nodes are often partially aligned . For examples, knowledge graphs share a number of named entities though they may have different relation schema; collaboration networks on publications and awarded projects share some researcher nodes who are authors and investigators, respectively; people use multiple web services, shopping, tweeting, rating movies, and some may register the same e-mail account across the platforms. In this article, we propose partially aligned graph convolutional networks to learn node representations across the models. We provide multiple methods such as model sharing, regularization, and alignment reconstruction, as well as theoretical analysis to positively transfer knowledge across the set of partially aligned nodes. Extensive experiments on real-world knowledge graphs, collaboration networks, and bipartite rating graphs show the superior performance of our proposed methods on relation classification, link prediction, and item recommendation.
A reliable climate model based on Gaussian process

Climate data is of great essence in various fields of researches. As direct observations are limited by sensing resolution and accuracy, a considerable amount of climate data is generated by theoretical computation. However, current mainstream climate models suffer from either limited accuracy or discrete outputs. In this paper, we propose a method of generating climate data by utilizing the properties of the Gaussian Process and carefully designing its kernel structure. Different from previous work, our model considers both temporal and spatial dimensions as inputs, hence being able to generate continuous predictions on certain areas or time intervals and achieve high accuracy. We conduct experiments on the famous ERA5 dataset and compare our method with classic model. Our model outperforms the classic one in terms of error scores in both spatial and temporal tasks, and comparison of uncertainty growth shows our advantage.
Optimizing recommender system using federated clustering

Currently, recommender systems are widely applied in various fields. However, due to the limited needs and special circumstances of users to be recommended, it is difficult for a recommender system to cover all users' interest lists at the same time. In this work, we present a kind of optimized federated clustering scheme (OP-Fed-Clustering) for users' private tendency data. The scheme starts by coding the initial data objects to protect privacy and then optimizes the assignment of data points based on object similarity. We also validates the algorithm's effectiveness on the FoodRecipe dataset and compares the algorithm to initial K-FED. Our tentative data show that the effectiveness of the proposed OP-Fed-Clustering algorithm, demonstrating universally superior performance while preserving user data confidentiality.
Text classification methods based on knowledge graph and BERT

The problem of text classification based on natural language processing has become a research hotspot, but the current text classification faces the problems of difficult extraction of key elements, fuzzy classification features and overlapping labels, and the commonly used text classification methods may have poor classification results. This article proposes classification studies for models that combine knowledge graph with the BERT model. The experimental results show that the K-BERT model incorporating knowledge graph achieves 88.2% correct classification rate on court verdicts, which is about 5% higher compared with the BERT model, which means that the model in this paper can improve the correct rate of text classification to a certain extent.
Few-Shot Knowledge Graph Completion Model Based on Relation Learning

Considering the complexity of entity pair relations and the information contained in the target neighborhood in few-shot knowledge graphs (KG), existing few-shot KG completion methods generally suffer from insufficient relation representation learning capabilities and neglecting the contextual semantics of entities. To tackle the above problems, we propose a Few-shot Relation Learning-based Knowledge Graph Completion model (FRL-KGC). First, a gating mechanism is introduced during the aggregation of higher-order neighborhoods of entities in formation, enriching the central entity representation while reducing the adverse effects of noisy neighbors. Second, during the relation representation learning stage, a more accurate relation representation is learned by using the correlation between entity pairs in the reference set. Finally, an LSTM structure is incorporated into the Transformer learner to enhance its ability to learn the contextual semantics of entities and relations and predict new factual knowledge. We conducted comparative experiments on the publicly available NELL-One and Wiki-One datasets, comparing FRL-KGC with six few-shot knowledge graph completion models and five traditional knowledge graph completion models for five-shot link prediction. The results showed that FRL-KGC outperformed all comparison models in terms of MRR, Hits@10, Hits@5, and Hits@1 metrics.
A new conjecture: the high-order difference of prime sequence approximately obeys the normal distribution

In this paper, a new conjecture is presented: The high-order difference of prime sequence approximately obeys the normal distribution. Moreover, the 78498 prime numbers less than 1000000 are discussed to provide numerical evidence for the new conjecture. This conjecture provides a new direction for the study of the properties of prime numbers.
Transformer-Based Object Detection with Deep Feature Fusion Using Carafe Operator in Remote Sensing Image

Recently, broad applications can be found in optical remote sensing images (ORSI), such as in urban planning, military mapping, field survey, and so on. Target detection is one of its important applications. In the past few years, with the wings of deep learning, the target detection algorithm based on CNN has harvested a breakthrough. However, due to the different directions and target sizes in ORSI, it will lead to poor performance if the target detection algorithm for ordinary optical images is directly applied. Therefore, how to improve the performance of the object detection model on ORSI is thorny. Aiming at solving the above problems, premised on the one-stage target detection model-RetinaNet, this paper proposes a new network structure with more efficiency and accuracy, that is, a Transformer-Based Network with Deep Feature Fusion Using Carafe Operator (TRCNet). Firstly, a PVT2 structure based on the transformer is adopted in the backbone and we apply a multi-head attention mechanism to obtain global information in optical images with complex backgrounds. Meanwhile, the depth is increased to better extract features. Secondly, we introduce the carafe operator into the FPN structure of the neck to integrate the high-level semantics with the low-level ones more efficiently to further improve its target detection performance. Experiments on our well-known public NWPU-VHR-10 and RSOD show that mAP increases by 8.4% and 1.7% respectively. Comparison with other advanced networks also witnesses that our proposed network is effective and advanced.
Graph Normalizing Flows to Pre-image Free Machine Learning for Regression

In Machine Learning, data embedding is a fundamental aspect of creating nonlinear models. However, they often lack interpretability due to the limited access to the embedding space, also called latent space. As a result, it is highly desirable to represent, in the input space, elements from the embedding space. Nevertheless, obtaining the inverse embedding is a challenging task, and it involves solving the hard pre-image problem. This task becomes even more challenging when dealing with structured data like graphs, which are complex and discrete by nature. This article presents a novel approach for graph regression using Normalizing Flows (NFs), in order to avoid the pre-image problem. By creating a latent representation space using a NF, the method overcomes the difficulty of finding an inverse transformation. The approach aims at supervising the space generation process in order to create a space suitable for the specific regression task. Furthermore, any result obtained in the generated space can be translated into the input space through the application of the inverse transformation learned by the model. The effectiveness of our approach is demonstrated by using a NF model on different regression problems. We validate the ability of the method to efficiently handle both the pre-image generation and the regression task.
An Efficient Entropy-Based Graph Kernel

Graph kernels are methods used in machine learning algorithms for handling graph-structured data. They are widely used for graph classification in various domains and are particularly valued for their accuracy. However, most existing graph kernels are not fast enough. To address this issue, we propose a new graph kernel based on the concept of entropy. Our method has the advantage of handling labeled and attributed graphs while significantly reducing computation time when compared to other graph kernels. We evaluated our method on several datasets and compared it with various state-of-the-art techniques. The results show a clear improvement in the performance of the initial method. Furthermore, our findings rank among the best in terms of classification accuracy and computation speed compared to other graph kernels.
A Non-retracted Path Generation Algorithm for Material Extrusion Type of Additive Manufacturing

Path planning algorithms are utilized in various fields to improve the efficiency of the defined tasks. Regarding extrusion based additive manufacturing (AM) technologies, an optimized path performs the same task in less time with higher surface quality and better mechanical properties. This paper proposes a continuous path planning algorithm for decreased manufacturing time and better surface quality of artifacts fabricated via extrusion-based AM. It is a versatile path-planning algorithm developed on Hierholzer’s algorithm for directed graphs. It generates a continuous path for any given graph representing the infill pattern of the parts to be fabricated via material extrusion. The performance of the proposed algorithm is compared with respect to the conventional Hierholzer’s algorithm and other similar algorithms over various cases. In the literature, complete elimination of u-turns is not observed so that the novelty of this work is to attain a continuous path planning algorithm that can eliminate both fast travels and u-turns. In addition to the advantages of continuous paths, the proposed method results in better surface quality due to its ability to omit u-turns during travel. The algorithm may also be used in laser-based AM technologies or any area coverage applications.
Detecting Image Forgery over Social Media Using U-NET with Grasshopper Optimization

Currently, video and digital images possess extensive utility, ranging from recreational and social media purposes to verification, military operations, legal proceedings, and penalization. The enhancement mechanisms of this medium have undergone significant advancements, rendering them more accessible and widely available to a larger population. Consequently, this has facilitated the ease with which counterfeiters can manipulate images. Convolutional neural network (CNN)-based feature extraction and detection techniques were used to carry out this task, which aims to identify the variations in image features between modified and non-manipulated areas. However, the effectiveness of the existing detection methods could be more efficient. The contributions of this paper include the introduction of a segmentation method to identify the forgery region in images with the U-Net model’s improved structure. The suggested model connects the encoder and decoder pipeline by improving the convolution module and increasing the set of weights in the U-Net contraction and expansion path. In addition, the parameters of the U-Net network are optimized by using the grasshopper optimization algorithm (GOA). Experiments were carried out on the publicly accessible image tempering detection evaluation dataset from the Chinese Academy of Sciences Institute of Automation (CASIA) to assess the efficacy of the suggested strategy. The results show that the U-Net modifications significantly improve the overall segmentation results compared to other models. The effectiveness of this method was evaluated on CASIA, and the quantitative results obtained based on accuracy, precision, recall, and the F1 score demonstrate the superiority of the U-Net modifications over other models.
VR Driven Unsupervised Classification for Context Aware Human Robot Collaboration

Human behavior, despite its complexity, follows structured principles that, if understood, will result in more reliable and effective collaborative automation environments. Characterizing human behavior in collaborative automation systems based on understanding the underlying context allows for novel advances in robotic human behavior sensing, processing, and predicting. Here, virtual reality, through integration of HTC Vive Arena Pro Eye Bundle, Leap Motion, and Unity 3D game engine, is used for safe and secure data collection on humans’ movements and body language in human robot collaborative environments. This paper proposes an unsupervised classification framework through integration of dynamic time warping and k-means clustering algorithm to enable robotics agents to understand humans’ intentions based on their body movements. Results display that the proposed framework is capable of identifying underlying intentions with an average accuracy, recall, and precision of 85%, 73%, and 75%, respectively.
Effect of Dust Events on Photovoltaics Electricity Production in the European and North African Regions

The effect of two dust events on solar energy has been studied in Europe and North Africa. For analysis, total aerosol and dust optical depth (AOD) in conjunction with solar irradiation and energy data were utilized from Copernicus Atmosphere Monitoring Service (CAMS). The AOD reached ~2.8 and ~1.7 during the dust events (16–19 April 2021 and 15–18 March 2022), respectively. Both events originated in North Africa, reaching the northern parts of Europe and the Black Sea. The energy production of solar photovoltaics in the affected regions was reduced by almost 18–36 GWh, followed by an estimated financial loss of close to 4 million euros.
Readiness of future teachers for successful professional activities is the result of effective management of the university's educational process

The article theoretically substantiates the strategy of managing the training of a successful personality of a future teacher, an important resource for improving the quality of the educational process of the university. The possibilities of managerial functions of a teacher in the formation of students' readiness for successful professional activity are revealed; readiness for successful professional activity is considered as a result of effective management of student training. The essence, content, structure, criteria and levels of formation of this complex personal formation are concretized; the structural-functional "Model of managing the formation of students' readiness for successful professional activity" was developed and tested, its effectiveness was experimentally confirmed. The possibilities of various forms of organization of the educational process (lectures, practical classes, trainings), methods, means of its implementation were checked; the role of management in the use of educational technologies is shown, which ensure the effectiveness of managing the professional training of students and the positive dynamics of the level of their readiness for successful pedagogical activity.
“The pingdemic has been a greater challenge than Covid itself”: semantic prosodies in UK newspaper articles during the pandemic

Abstract In July 2021, the UK recorded an increase in self-isolation notifications to the users of the NHS Covid-19 contact-tracing app. The term pingdemic was coined and used widely in the media to refer to this period of increased notifications. Whilst existing research on language evolution during Covid-19 focuses on the negative connotations of the term pandemic , little attention has been dedicated yet to the connotations of the term pingdemic . Thus, this study contributes a com- parison between the semantic prosody of pingdemic and its benchmark pandemic , through the corpus analysis of 628 UK printed newspaper articles, published during the peak in notifications to add to the increasing knowledge of how the pandemic evolved in the UK and how AI has had an impact on it. With the aid of the Sketch Engine, through the analysis of keywords, collocations and Word Sketch Difference visualisations, we found that pingdemic held a more negative semantic prosody than pandemic .
Nonlinear and non-CP gates for Bloch vector amplification

Abstract Any state r = ( x , y , z ) of a qubit, written in the Pauli basis and initialized in the pure state r = (0, 0, 1), can be prepared by composing three quantum operations: two unitary rotation gates to reach a pure state <?CDATA ${\boldsymbol{r}}={\left({x}^{2}+{y}^{2}+{z}^{2}\right)}^{-\tfrac{1}{2}}\times (x,y,z)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi mathvariant="bold-italic">r</mml:mi> <mml:mo>=</mml:mo> <mml:msup> <mml:mrow> <mml:mfenced close=")" open="("> <mml:mrow> <mml:msup> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>+</mml:mo> <mml:msup> <mml:mrow> <mml:mi>y</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>+</mml:mo> <mml:msup> <mml:mrow> <mml:mi>z</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:mfenced> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mstyle displaystyle="false"> <mml:mfrac> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:mfrac> </mml:mstyle> </mml:mrow> </mml:msup> <mml:mo>×</mml:mo> <mml:mo stretchy="false">(</mml:mo> <mml:mi>x</mml:mi> <mml:mo>,</mml:mo> <mml:mi>y</mml:mi> <mml:mo>,</mml:mo> <mml:mi>z</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:math> on the Bloch sphere, followed by a depolarization gate to decrease ∣ r ∣. Here we discuss the complementary state-preparation protocol for qubits initialized at the center of the Bloch ball, r = 0 , based on increasing or amplifying ∣ r ∣ to its desired value, then rotating. Bloch vector amplification increases purity and decreases entropy. Amplification can be achieved with a linear Markovian completely positive trace-preserving (CPTP) channel by placing the channel’s fixed point away from r = 0 , making it nonunital, but the resulting gate suffers from a critical slowing down as that fixed point is approached. Here we consider alternative designs based on linear and nonlinear Markovian PTP channels, which offer benefits relative to linear CPTP channels, namely fast Bloch vector amplification without deceleration. These gates simulate a reversal of the thermodynamic arrow of time for the qubit and would provide striking experimental demonstrations of non-CP dynamics.
One-shot Based Knowledge Graph Embedded Neural Architecture Search Algorithm

The quality of embeddings is crucial for downstream tasks in knowledge graphs. Researchers usually introduce neural network architecture search into knowledge graph embedding for machine automatic construction of appropriate neural networks for each dataset. An existing approach is to divide the search space into macro search space and micro search space. The search strategy for micro space is based on one-shot weight sharing strategy, but it will lead to all the information obtained from the previous supernet training is discarded and the advantages of one-shot algorithm are not fully utilized. In this paper, we conduct experiments on common datasets for two important downstream tasks of knowledge graph embedding entity alignment and link prediction problems, respectively-and compare the search performance with existing manually designed neural networks as well as good neural network search algorithms. The results show that the improved algorithm can search better architectures for the same time when experiments are performed on the same dataset; the improved algorithm takes less time to search architectures with similar performance. Also, the improved algorithm searched the model on the dataset due to the human optimal level.
Review of Medical Image Segmentation Methods

Medical image is one of the key factors in the process of medical diagnosis and treatment. By analyzing the medical image obtained, doctors make judgments on the patient's condition and plan the next treatment process. Medical image segmentation is the process of segmenting areas of interest in medical images according to specific needs, which is a key step in medical image processing and analysis. With the great improvement of computer processing power, how to quickly and effectively segment huge image data and mining valuable information is the research hotspot of segmentation algorithm at present. This paper introduces several traditional medical image segmentation methods such as threshold method and region method, as well as convolutional neural network and Transformer segmentation method based on deep learning technology. Finally, this paper makes a summary and prospects for future development.