Reviewerly/exHarmony huggingface dataset:
Simple realization of a hybrid controlled–controlled-Z gate with photonic control qubits encoded via eigenstates of the photon-number parity operator

We propose a simple method to realize a hybrid controlled–controlled-Z (CCZ) gate with two photonic qubits simultaneously controlling a superconducting (SC) target qubit, by employing two microwave cavities coupled to a SC ququart (a four-level quantum system). In this proposal, each control qubit is a photonic qubit, which is encoded by two arbitrary orthogonal eigenstates (with eigenvalues ±1, respectively) of the photon-number parity operator. Since the two arbitrary encoding states can take various quantum states, this proposal can be applied to realize the hybrid CCZ gate, for which the two control photonic qubits can have various encodings. The gate realization is quite simple because only a basic operation is needed. During the gate operation, the higher energy intermediate levels of the ququart are not occupied, and, thus, decoherence from these levels is greatly suppressed. We further discuss how to apply this gate to generate a hybrid Greenberger–Horne–Zeilinger (GHZ) entangled state of a SC qubit and two photonic qubits, which takes a general form. As an example, our numerical simulation demonstrates that high-fidelity generation of a cat–cat–spin hybrid GHZ state is feasible within current circuit QED technology. This proposal is quite general, which can be applied to realize the hybrid CCZ gate as well as to prepare various hybrid GHZ states of a matter qubit and two photonic qubits in other physical systems, such as two microwave or optical cavities coupled to a four-level natural or artificial atom.
An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector

Anomaly detection plays a crucial role in preserving industrial plant health. Detecting and identifying anomalies helps prevent any production system from damage and failure. In complex systems, such as oil and gas, many components need to be kept operational. Predicting which parts will break down in a time interval or identifying which ones are working under abnormal conditions can significantly increase their reliability. Moreover, it underlines how the use of artificial intelligence is also emerging in the process industry and not only in manufacturing. In particular, the state-of-the-art analysis reveals a growing interest in the subject and that most identified algorithms are based on neural network approaches in their various forms. In this paper, an approach for fault detection and identification was developed using a Self-Organizing Map algorithm, as the results of the obtained map are intuitive and easy to understand. In order to assign each node in the output map a single class that is unique, the purity of each node is examined. The samples are identified and mapped in a two-dimensional space, clustering all readings into six macro-areas: (i) steady-state area, (ii) water anomaly macro-area, (iii) air-water anomaly area, (iv) tank anomaly area, (v) air anomaly macro-area, (vi) and steady-state transition area. Moreover, through the confusion matrix, it is found that the algorithm achieves an overall accuracy of 90 per cent and can classify and recognize the state of the system. The proposed algorithm was tested on an experimental plant at Università Politecnica delle Marche.
Trajectories Generation for Unmanned Aerial Vehicles Based on Obstacle Avoidance Located by a Visual Sensing System

In this work, vectorial trajectories for unmanned aerial vehicles are completed based on a new algorithm named trajectory generation based on object avoidance (TGBOA), which is presented using a UAV camera as a visual sensor to define collision-free trajectories in scenarios with randomly distributed objects. The location information of the objects is collected by the visual sensor and processed in real-time. This proposal has two advantages. First, this system improves efficiency by focusing the algorithm on object detection and drone position, thus reducing computational complexity. Second, online trajectory references are generated and updated in real-time. To define a collision-free trajectory and avoid a collision between the UAV and the detected object, a reference is generated and shown by the vector, symmetrical, and parametric equations. Such vectors are used as a reference in a PI-like controller based on the Newton–Euler mathematical model. Experimentally, the TGBOA algorithm is corroborated by developing three experiments where the F-450 quadcopter, MATLAB® 2022ª, PI-like controller, and Wi-Fi communication are applied. The TGBOA algorithm and the PI-like controller show functionality because the controller always follows the vector generated due to the obstacle avoidance.
A study on the application of hierarchical analysis in the analysis of factors influencing the quality of English teaching

At this stage, along with the continuous development of China's Internet information technology, the field of education and teaching has gradually penetrated computer technology to assist teaching, and the traditional teaching mode and evaluation mode have also undergone significant changes.This paper proposes an analysis method of English teaching quality influencing factors by combining intelligent algorithms, using hierarchical analysis to analyze relevant influencing factors, combining with the comprehensive evaluation of convenience rights, studying English teaching effect evaluation, building a diversified evaluation system, and completing the calculation and evaluation of teaching data. In the complex English teaching evaluation environment, in order to improve the accuracy and rationality of the evaluation model, the relevant personnel need to constantly adjust the English assessment objectives, so as to provide corresponding reference for the subsequent innovation and application of the teaching evaluation model.
Text presentation and information processing in Russian

This study is based on the Cognitive Theory of Multimedia Learning (Mayer 2009).We investigated how readers process and comprehend information when reading and listening texts of different formats: infographics, audiotext, infographics combined with audiotext, written format.Biographies of four Russian writers were used as the material.All the stimuli were of the same size and the same level of readability.In a four-group design experiment, 32 foreign students and 32 native speakers of Russian examined four texts in four different formats, answered the factual and analytical afterthe-text questions, gave the keywords and estimated the difficulty of each text.The overall results made it possible to build a hierarchy of best-perceived formats for both groups of participants.
Dynamic Exploratory Search for the Information Retrieval Anthology

This paper presents dynamic exploratory search technology for the analysis of scientific corpora. The unique dynamic features of the system allow users to analyze quantitative corpus statistics beyond document counts, and to switch between corpus exploration and corpus filtering. To demonstrate the innovation of our approach, we apply our technology to the IR Anthology, a comprehensive corpus of information retrieval publications. We showcase, among others, how to query for potential PC members and the “Salton number” of an author.
Use of mobile applications in citizen security

An abstract is often presented separately from the article, so it must be able to in the following article the use of the mobile application is recommended as a tool that serves public safety because currently crimes of extortion and fraud are committed by making false calls to people's mobile devices. Therefore, the objective of this research is to help families with their welfare and safety and motivate the use of mobile applications. In the present work the situation, task, action, dan result (STAR) methodology was applied to elaborate the present work, this technique will allow improving the difficulties of the people. This methodology is very important because it is used to evaluate the circumstances in which people find themselves, a sequence must be followed to achieve the proposed objectives. The result obtained is an average of 84.5% on the design of the mobile prototype; being approved by the experts. The beneficiaries of the research are citizens through the mobile application.
Hierarchical MPC-based Motion Planning for Automated Vehicles in Parallel Autonomy

Automated vehicles with parallel autonomy show advantages over fully automated vehicles and manual driving. This paper proposes a hierarchical motion planning method that mixes inputs of human drivers and the automated driving systems for automated vehicles in scenarios such as multi-lane roads and multi-intersections with dynamic obstacles. The proposed method comprises a reference path generator in the upper level and a nonlinear model predictive controller with mixed human-vehicle control in the lower level. The path planner considers dynamic obstacles, static obstacles, and human comfort to generate a reference path composed of splines with continuous curvatures in the upper level. In the lower level, the MPC generates a trajectory by tracking the reference path and optimizing the cost function containing inputs of drivers while avoiding both dynamic and static obstacles. The simulation verifies the efficacy and the computational tractability of the proposed method.
sFuzz2.0: Storage‐access pattern guided smart contract fuzzing

Abstract Smart contracts are distributed self‐enforcing programs which execute on top of blockchain networks. They have the potential to revolutionize many industries and have already been adopted for applications such as distributed finance and crowdfunding. Because smart contracts are immutable once they are deployed, it is important to identify and eliminate code vulnerabilities in smart contracts systematically. In this work, we propose sFuzz2.0 , a storage‐access‐pattern guided adaptive fuzzer based on sFuzz. sFuzz2.0 is motivated by the fact that certain vulnerabilities only manifest in the presence of certain function call sequences (as well as particular arguments). Given that there are exponentially many function call sequences, sFuzz randomly generates sequences without guidance. As a result, the probability of discovering those vulnerabilities is negligible. sFuzz2.0 tackles the problem with two approaches, that is, by generating function call sequences that trigger different storage‐access patterns passively (i.e., by prioritizing seeds which cover new patterns) or actively (i.e., by actively seeking out different patterns). The experiment results suggest that the passive strategy outperforms sFuzz by achieving better code coverage (i.e., 37.53%) and discovering more vulnerabilities (i.e., 20.49%).
Bootstrap State Representation Using Style Transfer for Better Generalization in Deep Reinforcement Learning

Deep Reinforcement Learning (RL) agents often overfit the training environment, leading to poor generalization performance. In this paper, we propose Thinker, a bootstrapping method to remove adversarial effects of confounding features from the observation in an unsupervised way, and thus, it improves RL agents’ generalization. Thinker first clusters experience trajectories into several clusters. These trajectories are then bootstrapped by applying a style transfer generator, which translates the trajectories from one cluster’s style to another while maintaining the content of the observations. The bootstrapped trajectories are then used for policy learning. Thinker has wide applicability among many RL settings. Experimental results reveal that Thinker leads to better generalization capability in the Procgen benchmark environments compared to base algorithms and several data augmentation techniques.
Design of English Intelligent Information Teaching System Based on Improved Glr Algorithm

At present, with the rapid development of English classroom teaching reform and innovation. Great changes are taking place in English education and training. Teachers must not only understand professional knowledge, but also understand and apply the teaching strategies in the information learning environment. This paper focuses on the scientific research of the improved GLR algorithm, which is used as the basic design of English intelligent information teaching system. This paper expounds the shortcomings of traditional CAI teaching courseware and the characteristics of ICAI learning software in the world at present, and gives the motivation and value of developing WBIETS system software. This paper describes the comprehensive basic theoretical design and development principles of ICAI. In this paper, GLR algorithm is improved to make its translation more accurate, and this feature is explored through experiments. The experiment shows that in the English intelligent information teaching system built by this article, the English translation of the improved GLR algorithm can accurately read more than 98% of the English translation of the article, greatly improving students' learning efficiency. In the actual teaching achievement test, the pass rate of the students who studied through the system of this article reached 100%, and the excellent rate also reached 26%. The creation and traditional conventional teaching methods have greatly improved.
Internal service of an enterprise admission control

Many organizations have a need of exposing their internal services to its employees to facilitate work from anywhere. Being able to access these services from any device or network has always made implementing safe access control challenging. User-based access control through the use of multi-factor authentication is typically used to create access control for such purposes. Research suggests a safe network access control method for granting access to internal services to a certain group of devices that abide by the security guidelines of an organisation. It tries to improve on current port knocking techniques by making services available to a specified group of devices rather than merely a small group of people.
Bilaterally Slimmable Transformer for Elastic and Efficient Visual Question Answering

Recent advances in Transformer architectures [1] have brought remarkable improvements to visual question answering (VQA). Nevertheless, Transformer-based VQA models are usually deep and wide to guarantee good performance, so they can only run on powerful GPU servers and cannot run on capacity-restricted platforms such as mobile phones. Therefore, it is desirable to learn an elastic VQA model that supports adaptive pruning at runtime to meet the efficiency constraints of different platforms. To this end, we present the bilaterally slimmable Transformer (BST), a general framework that can be seamlessly integrated into arbitrary Transformer-based VQA models to train a single model once and obtain various slimmed submodels of different widths and depths. To verify the effectiveness and generality of this method, we integrate the proposed BST framework with three typical Transformer-based VQA approaches, namely MCAN [2], UNITER [3], and CLIP-ViL [4], and conduct extensive experiments on two commonly-used benchmark datasets. In particular, one slimmed MCAN <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_\mathsf {BST}$</tex-math></inline-formula> submodel achieves comparable accuracy on VQA-v2, while being 0.38× smaller in model size and having 0.27× fewer FLOPs than the reference MCAN model. The smallest MCAN <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_\mathsf {BST}$</tex-math></inline-formula> submodel only has 9M parameters and 0.16G FLOPs during inference, making it possible to deploy it on a mobile device with less than 60 ms latency.
Constrained k-means algorithm for resource allocation in mobile cloudlets

With the rapid increase in the number of mobile devices connected to the Internet in recent years, the network load is increasing.As a result, there are significant delays in the delivery of cloud resources to mobile users.Edge computing technologies (edge, cloudlet, fog computing, etc.) have been widely used in recent years to eliminate network delays.This problem can be solved by allocating cloud resources to the cloudlets that are close to users.The article proposes a clustering-based model for the optimal allocation of cloud resources among cloudlets.The proposed model takes into account user activity, usage frequency of cloud resources, the physical distance between users and cloud resources, as well as the storage capacity of cloudlets for optimal allocation of cloud resources in cloudlets.The proposed model was formalized as a constrained k-means method and an algorithm was developed to solve it.The MATLAB 2022a toolkit was used to evaluate the efficiency of the proposed algorithm.The obtained results revealed that the algorithm is promising.
Two-stage Detection of Semantic Redundancies in RDF Data

With the enrichment of the RDF (resource description framework), integrating diverse data sources may result in RDF data duplication. Failure to effectively detect the duplicates brings redundancies into the integrated RDF datasets. This not only increases unnecessarily the size of the datasets, but also reduces the dataset quality. Traditionally a similarity calculation is applied to detect if a pair of candidates contains duplicates. For massive RDF data, a simple similarity calculation may lead to extremely low efficiency. To detect duplicates in the massive RDF data, in this paper we propose a detection approach based on RDF data clustering and similarity measurements. We first propose a clustering method based on locality sensitive hashing (LSH), which can efficiently select candidate pairs in RDF data. Then, a similarity calculation is performed on the selected candidate pairs. We finally obtain the duplicate candidates. We show through experiments that our approach can quickly extract the duplicate candidates in RDF datasets. Our approach had the highest F score and time performance in the OAEI (Ontology Alignment Evaluation Initiative) 2019 competition.
A Knowledge Graph Embedding Framework With Triple Semantics

The knowledge graph embedding model aims to use low-dimensional real-valued vectors to represent the entities and relations in the triples, where operations such as link prediction and triple classification can be performed based on these representations. However, existing embedding models only consider the structural embedding of triples, while ignoring the semantic information of triples. This paper proposes a knowledge graph embedding learning framework combined with triple semantic information (KGSE). KGSE comprehensively considers the structural embedding and semantic embedding of triples, where semantic embedding is used as a supplement to improve the quality of embedding. Specifically, KGSE uses the improved TransD model to obtain the structural embedding of triples, and employs the deep convolutional neural model combined with an attention mechanism to obtain the semantic embedding of triples. In addition, a novel energy function is designed to jointly train the above two embeddings. Experimental results show that the proposed framework improves significantly compared with Trans-based models and other baseline models in link prediction and triple classification tasks, which verifies the effectiveness of the proposed framework.
HostileNet: Multilabel Hostile Post Detection in Hindi

In this article, we deal with the task of hostile post detection in Hindi. The objective is to predict whether a social media post is hostile or not. Furthermore, if the post is hostile, we identify one or more fine-grained hostile dimensions out of the following four— <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fake</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hate</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">offensive</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">defamation</i> . We propose, a novel deep-learning framework that leverages HindiBERT-based contextual representations and hand-crafted features like lexicon, emoticon, and hashtag embeddings for hostile post classification. Moreover, we also propose a novel mechanism to fine-tune HindiBERT’s attention vectors with respect to each hostile dimension. We evaluate on the shared task dataset on hostile post detection in Hindi for both coarse-grained (hostile versus nonhostile) and fine-grained ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fake</i> versus <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hate</i> versus <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">offensive</i> versus <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">defamation</i> ) setups. outperforms the best-performing system as reported in the shared task for both the setups. Furthermore, we provide a thorough analysis of the obtained results in the form of an ablation study, error analysis, attention heatmap analysis, lexicon feature analysis, and so on. We also perform in-the-wild evaluation and conduct a user survey to assess the robustness of our proposed model. We make the code and the curated multilabel hostile lexicon available for research use at https://github.com/LCS2-IIITD/HostileNet.
Correct and efficient UAV missions based on temporal planning and in-flight hybrid simulations

Controller synthesis has been successfully applied in UAV applications, to construct a mission plan that is guaranteed to be correct with respect to a user-provided specification. Albeit being correct, these plans may not be optimal in the vehicle’s trajectory, battery consumption, or other criteria which the user may consider relevant. A possibility would be to apply a quantitative synthesis approach where the target is to compute efficient plans before the mission, at a higher cost of complexity and potential limitations in the optimization goals to achieve. As an alternative, in this paper we propose doing the plan optimization in-flight. For this, we use available tools that synthesize controllers with multiple controllable choices and later select among these choices in-flight using hybrid simulations ranking them according to the optimization objective. We present the advantages of our approach and validate them using software-in-the-loop simulation with typical UAV mission scenarios.
Bayesian quantum parameter estimation with Gaussian states and homodyne measurements in a dissipative environment

The deterioration of precision caused by quantum decoherence in dissipative environments is a longstanding problem in the development of quantum metrology. Therefore, it is significant to effectively analyze the evolution of quantum systems under dissipative environments and quantitatively estimate the parameters given a certain probe. Here, we propose a continuous-variable (CV) quantum metrology scheme within the Bayesian paradigm to estimate phase shift, loss coefficient and temperature of a dissipative environment. Through numerical evaluation and concrete analysis, we investigate the achievable precision of Bayesian quantum parameter estimation with Gaussian states and homodyne measurements, and the influence of different parameters on the estimation process. More interestingly, the scheme can be applied in more practical scenarios where not enough measurements are obtained and the frequentist analysis is not well defined.