Reviewerly/exHarmony huggingface dataset:
Ways of transmitting modal meanings in Dutch-Russian translation: The case of the modal particle maar

In this study, we examine the strategies translators have at their disposal to render the meaning of Dutch modal particles in the Russian language. Modal particles are difficult to translate because the meaning of these words is abstract, highly context-dependent and sometimes open to multiple interpretations. Based on parallel Dutch-Russian texts we show which strategies translators use to express the functions of the Dutch modal particle maar ‘but’ in Russian. The modal particle maar is very often omitted in translations, because it has no equivalent in Russian and the image of reality given by the sentence does not become different in a translation when a modal particle is not translated. We find that maar is most often omitted in the function of mitigating the imperative. Translators compensate for the lack of an appropriate modal particle by using verbs with a narrower meaning in Russian which allows them to more accurately describe the drawn context. We also find that in some cases, translators make use of lexical and grammatical transformation such as using prefixes in Russian to soften the imperative, rather than omitting maar completely. In Russian, there are many possibilities to convey modal meaning in lexico-grammatical ways, for example using particles, verbs with a narrower meaning and prefixes that soften the imperative; modal verbs as such can take on the function of expressing modality, and also the verb tense can be used to convey modality.
Functional Distributional Semantics at Scale

Functional Distributional Semantics is a linguistically motivated framework for modelling lexical and sentence-level semantics with truthconditional functions using distributional information.Previous implementations of the framework focus on subject-verb-object (SVO) triples only, which largely limits the contextual information available for training and thus the capability of the learnt model.In this paper, we discuss the challenges of extending the previous architectures to training on arbitrary sentences.We address the challenges by proposing a more expressive lexical model that works over a continuous semantic space.This improves the flexibility and computational efficiency of the model, as well as its compatibility with present-day machine-learning frameworks.Our proposal allows the model to be applied to a wider range of semantic tasks, and improved performances are demonstrated from experimental results.
Research on trajectory planning method of the collaborative manipulator

To make the operation trajectory of the collaborative manipulator smoother and solve the problems of turning point displacement and velocity abrupt change, the 5-3-5 joint interpolation method is used to transition the operation trajectory. First, establishing a robotic arm simulation model used an improved D-H parameter method in the MATLAB Robotics toolbox. Then, to verify the rationality of the model structure of the robotic arm, the forward and reverse kinematics analysis is carried out, and the reachable space is analyzed by the Monte Carlo method. Finally, the gripping trajectory simulation of the collaborative robotic arm is carried out. The simulation results show that the operation trajectory after the transition processing of the 5-3-5 joint interpolation method can make the operation of the collaborative manipulator more stable.
Electrical insulator identification method in multiple images based on two stage CNN network

Ultraviolet and infrared images are widely used to detect potential defects of electric equipment in advance. However, With the continuous expansion of the scale of electrical equipment, almost all the data is still uses manual analysis, which has some shortcoming example high cost. In order to improve the intelligence level of electric equipment detection, this paper proposes a multiple images identification method for electrical insulator based on two stage cnn network. First, a deep convolutional neural network identification model is built based on Faster-RCNN with initial weights in VGG. In addition, combine with the dataset expansion method example rotation, 462 images of electrical insulator are used for training. Finally, this model is used to identification image of equipment. The experimental show that the method proposed in this paper for electrical insulator map accuracy reaches 96.51%. Therefore, the proposed method can improve the identification accuracy, and provides condition for electrical equipment state detect.
Universal Fourier optics model for virtual coherence scanning interferometers

Optical surface topography measurements sometimes suffer from systematic errors. In order to predict such deviations, modeling of optical profilers is a substantial part of the European project TracOptic (Traceable Optics). Within the framework of this project, we recently developed the UFO (Universal Fourier Optics) model, which simulates virtual CSI measurements of surface topographies that fulfill the requirements of the scalar Kirchhoff approximation. The model enables a fast computation of ‘measured’ surface topographies as it is based on discrete Fourier transforms. It treats the surface under investigation as a two-dimensional phase object assuming a linear dependence of the interference phase on surface height and axial spatial frequency. The scattered light field is transferred to the Fourier domain and multiplied by a partial two-dimensional transfer function (TF) representing a horizontal cross section of the three-dimensional TF at a certain axial spatial frequency or evaluation wavelength, respectively. The TF includes parameters of the interference microscope and the reference field distribution. Inverse Fourier transform enables the reconstruction of the phase object. The coherence peak position of an interference signal results from numerical differentiation with respect to the axial spatial frequency and is generally used to overcome the 2π ambiguity of the phase profile. Parameters affecting final results of reconstructed surface topographies are the central wavelength and the spectral bandwidth of the illuminating light as well as the numerical aperture of the objective lens and the chosen evaluation wavelength. We discuss results of the UFO model with respect to the prediction of systematic deviations of measured surface topographies.
ViWise: Fusing Visual and Wireless Sensing Data for Trajectory Relationship Recognition

People usually form a social structure (e.g., a leader-follower, companion, or independent group) for better interactions among them and thus share similar perceptions of visible scenes and invisible wireless signals encountered while moving. Many mobility-driven applications have paid much attention to recognizing trajectory relationships among people. This work models visual and wireless data to quantify the trajectory similarity between a pair of users. We design a visual and wireless sensor fusion system, called ViWise, which incorporates the first-person video frames collected by a wearable visual device and the wireless packets broadcast by a personal mobile device for recognizing finer-grained trajectory relationships within a mobility group. When people take similar trajectories, they usually share similar visual scenes. Their wireless packets observed by ambient wireless base stations (called wireless scanners in this work) usually contain similar patterns. We model the visual characteristics of physical objects seen by a user from two perspectives: micro-scale image structure with pixel-wise features and macro-scale semantic context. However, we model characteristics of wireless packets based on the encountered wireless scanners along the user’s trajectory. Given two users’ trajectories, their trajectory characteristics behind the visible video frames and invisible wireless packets are fused together to compute the visual-wireless data similarity that quantifies the correlation between trajectories taken by them. We exploit modeled visual-wireless data similarity to recognize the social structure within user trajectories. Comprehensive experimental results in indoor and outdoor environments show that the proposed ViWise is robust in trajectory relationship recognition with an accuracy of above 90%.
An unmanned vehicle path planning problem based on intelligent algorithms

In the current climate of traffic congestion and frequent traffic accidents due to increasing pressure on transport, the birth and development of driverless cars is particularly important. However, as the technologies of unmanned vehicles are not yet perfect, it becomes more practical to carry goods at low speed - unmanned delivery - than to carry people at high speed. In order to achieve the goal of making full use of the existing road resources and improving the efficiency of distribution in logistics, this paper uses an intelligent algorithm - the Pelican algorithm - to optimise the cost of the unmanned vehicle and solve for its minimum cost.
UltraSnoop: Placement-agnostic Keystroke Snooping via Smartphone-based Ultrasonic Sonar

Keystroke snooping is an effective way to steal sensitive information from the victims. Recent research on acoustic emanation-based techniques has greatly improved the accessibility by non-professional adversaries. However, these approaches either require multiple smartphones or require specific placement of the smartphone relative to the keyboards, which tremendously restricts the application scenarios. In this article, we propose UltraSnoop, a training-free, transferable, and placement-agnostic scheme that manages to infer user’s input using a single smartphone placed within the range covered by a microphone and speaker. The innovation of Ultrasnoop is that we propose an ultrasonic anchor-keystroke positioning method and a Mel Frequency Cepstrum Coefficients clustering algorithm, synthesis of which could infer the relative position between the smartphone and the keyboard. Along with the keystroke time difference of arrival, our method could infer the keystrokes and even gradually improve the accuracy as the snooping proceeds. Our real-world experiments show that UltraSnoop could achieve more than 85% top-3 snooping accuracy when the smartphone is placed within the range of 30–60 cm from the keyboard.
Writing strategies in the production of expository dissemination articles in university students

Academic writing fulfills an epistemic function as it transforms knowledge, creates ideas and becomes a tool for new and significant learning. However, academic writing is very challenging for university students. Therefore, this study aims to determine how writing strategies affect students at a university in Lima, Peru who produce expository dissemination articles. This research used a quantitative approach, an applied study and an experimental design with quasi-experimental characteristics. The sample consisted of 56 second-semester students, 26 of whom belonged to the control group and 30 to the experimental group. The instrument and the programme of 14 learning sessions were applied in the Zoom virtual classroom through synchronous and asynchronous classes. The results showed a value of U= (Z = -5.262 &lt;-1.96) and (p = 0.00 &lt; 0.05) which demonstrates that the programme applied in virtual environments had a significant effect on the experimental group. The four writing processes of planning, textualization, revision and editing are integrated into academic research in the educational field. This program’s aim is to improve and strengthen university students writing skills.
Comparison of Convolutional Neural Network Architecture on Detection of Helmet Use by Humans

The helmet is one of the protective equipment for the head when driving. Although it is a protector, some criminals misuse helmets to disguise their identities, such as robbery at ATMs. Some places have put a sticker not to wear a helmet in the ATM room. However, this advice is often violated. The research adopted the Convolutional Neural Network (CNN) algorithm to identify humans who use helmets and do not use helmets based on digital images. Several CNN models, such as MobileNet-V2, ResNet-50, and VGG-16, were compared in performance. The experiment was carried out using a dataset consisting of 3,207 images which were divided into two classes. The first class is used for classifying human images using helmets with 1,603 images. At the same time, the second class is for images of humans who do not use helmets, with a total of 1,604 images. The test results show that the architecture with the highest accuracy value is ResNet-50, 97.81%. At the same time, the mobileNet-V2 architecture obtained a lower accuracy value of 96.36% and the VGG-16 architecture of 52.25%.
Cross-Border Payment Based on Blockchain Technology and Digital Currency

With the rapid development of financial globalization and currency globalization, trade exchanges between various countries are increasing, and the demand for cross-border payment in multiple countries is increasing, which leads to a series of issues related to cross-border payment, such as payment efficiency, payment cost, and payment success rate, etc. Therefore, blockchain technology is introduced, a decentralized, non-tamperable, and traceable distributed accounting technology. Based on improving the current cross-border payment system, this paper studies the use of blockchain technology to solve related problems in the traditional cross-border payment model in legal digital currency (Central Bank Digital Currency) and currency digitization (fund lending). Through the reading of many documents and the search for information, the application of blockchain technology combined with digital currency has broad development prospects, which will completely improve the existing payment model and achieve a more efficient and safer transaction environment. However, the current development of this technology is still in its infancy, and no country or region has yet achieved large-scale operation of this technology. The technology still needs to be improved in terms of practice, unified consensus algorithm, and legal supervision.
BERD+: A Generic Sequential Recommendation Framework by Eliminating Unreliable Data with Item- and Attribute-level Signals

Most sequential recommendation systems (SRSs) predict the next item as the target for users given its preceding items as input, assuming the target is definitely related to its input. However, users may unintentionally click items that are inconsistent with their preference due to external factors, causing unreliable instances whose target mismatches the input. We, for the first time , verify SRSs can be misguided by such unreliable instances and design a generic SRS framework B y E liminating un R eliable D ata (BERD+), which can be flexibly plugged into existing SRSs. Specifically, BRED+ is guided with observations on the training process of instances: Unreliable instances generally have high training loss; high-loss instances are not necessarily unreliable but uncertain ones caused by blurry sequential patterns; and item attributes help rectify instance loss and uncertainty, but may also introduce disturbance. Accordingly, BERD+ models both the loss and uncertainty of each instance via a Gaussian distribution, whereby a heterogeneous uncertainty-aware graph convolution network is designed to learn accurate embeddings for different entities while reducing the disturbance caused by uncertain attribute values. Thereafter, an explicit preference extractor rectifies instance loss and uncertainty and reduces the disturbance caused by less-focused attribute types. Finally, instances with high loss and low uncertainty are eliminated as unreliable data. Extensive experiments verify the efficacy of BERD+.
Unmanned cart path planning based on improved A* algorithm

With the maturity of robotics, manual inspection is gradually replaced by inspection robots in large storage centres and other places where goods are densely packed. Aiming at the problems of low efficiency, high number of inflection points and inability to perform real-time obstacle avoidance in the traditional A* algorithm for mobile robot path planning, this paper proposes an improved A* search algorithm. Measures such as modification of the heuristic function, optimisation of the open list and curve smoothing are used to effectively improve the search efficiency.
Multi-target points path planning for fixed-wing unmanned aerial vehicle performing reconnaissance missions

In this paper, we study the path planning problem of a fixed-wing unmanned aerial vehicle (UAV) with a reconnaissance camera when performing a reconnaissance mission in airspace containing a no-fly zone. Due to the UAV's dynamic constraints and the reconnaissance mission's specificity, the problem can be formulated as a special variant of the traveling salesman problem (TSP), called the dynamic-constrained TSP with neighborhoods (DCTSPN). To solve this problem, the authors propose a hierarchical algorithm based on deep reinforcement learning (DRL), divided into an optimal sequence planning layer and a shortest path planning layer. In the optimal sequence planning layer, Firstly, the neighborhood boundary of the target point is discretized to form multiple reconnaissance points; then, the complex trajectory planning problem is simplified to planning on a finite directed graph by randomly selecting a finite set of reconnaissance points from the neighborhood boundary of the target point set. The double deep Q network (DDQN) algorithm is used to solve for the target point traversal sequence and the reconnaissance points for each target. In the shortest path planning layer, Use the Deep Deterministic Policy Gradient (DDPG) algorithm to perform global path planning in continuous state space and action space and generate a dynamically feasible optimal flight trajectory under the guidance of the optimal reconnaissance sequence to complete the given reconnaissance mission, Avoid no-fly zones. The simulation results show that the hierarchical algorithm is highly applicable and efficient.
An end-to-end system for transcription, translation, and summarization to support the co-creation process. A Health CASCADE Study.

This paper presents a web service and a deep learning (DL) pipeline that has been developed and implemented as part of the MSCA Health CASCADE project. The purpose of the web service is to provide support, streamline, and enable participatory methods, such as co-creation, in the public health domain. The DL pipeline assists with translation, transcription, speaker diarization, relation extraction, and summarization of audio recordings. This is achieved by removing the need for time-consuming tasks, such as translating and transcribing audio recordings. Additional value is created by extracting implicit relations from the transcribed text and identifying patterns, key themes, and trends. Finally, providing summaries of the transcripts creates a sense of ownership that can improve stakeholder retention in participatory methods.
Quantum Rotation Gate-Based Particle Swarm Algorithm for Test Data Anomaly Detection Model Hyperparameter Optimization

Particle Swarm Optimization (PSO) is a popular algorithm used for optimization problems in various domains. However, the PSO algorithm suffers from some limitations, e.g., premature convergence and slow convergence rate. In this paper, we propose a new algorithm called Quantum Rotation Gate Particle Swarm Optimization (QPSO), which is based on the quantum rotation gate to address these shortcomings. The proposed QPSO algorithm aims to improve the optimization capability of the original PSO. We applied QPSO to optimize the neural network architecture for numerical anomaly detection on the self-noise aerodynamic wind tunnel test data set. Good results are achieved and the effectiveness of the proposed method is demonstrated. Compared to the original PSO algorithm, QPSO outperforms it in terms of convergence rate and accuracy.
Estimating Redundancy-Reliability of CNNs Based on Strip-Median Attributes

Redundancy-reliability calculation for deep neural networks is of great importance for their lightweight operation. This article proposes a method for calculating the redundancy-reliability of convolutional neural networks (CNNs) based on strip-median attributes. First, based on the common cause principle, an input-sample-oriented reliability calculation method with automatic boundary detection is constructed to measure the network reliability differences caused by normal classification boundary changes. Next, the adaptive clustering algorithm is used and combined with strip-median attributes to identify the redundant strips of each convolutional layer (CONV) in the networks. Then, based on the central limit theorem and the Pauta criterion, a fault injection method with adaptive convergence ability is constructed to effectively calculate the soft-error-oriented redundancy-reliability of CNNs. The tests of multiple widely used CNNs with multiple datasets showed that with the results of the Monte Carlo (MC) model as the references, the reliability calculation accuracy of the proposed method was approximately 0.9834 and was 3.0% higher than that of the logical flips at bit level (LFBL) method. Additionally, the identification accuracy of the proposed method for redundant nodes in CNNs was better than that of the HRank method and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1 \times N$ </tex-math></inline-formula> pruning method, by average factors of approximately 14.32 and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.55\times $ </tex-math></inline-formula> , respectively. Furthermore, the redundant protection and the adaptive layer geometric center searching mechanism designed by the proposed method were effective. Thus, the proposed method can effectively achieve a one-to-one correspondence between the redundancy and reliability of CNNs.
Blockchain-Based Decentralized Reputation Framework: Understanding the Residents’ Satisfaction About Living House with Trustworthiness Consideration

Learning the residents’ perspectives on living houses and their satisfaction feedback is meaningful for enterprise reputation service. Some channels (systems of surveys) have already been established in our daily life to help residents publish the residents’ satisfaction feedback. However, related satisfaction feedback data’s authenticity and integrity cannot be guaranteed in centralized architecture channels currently, which limits the authenticity of reputation judgment of related enterprises (e.g., the realtor company, the tenement company, and others) building organizations. To track the issues, a blockchain-based decentralized reputation framework is designed to ensure the tamper-proof and traceability of residence satisfaction feedback. The framework involves two parts: (1) building a residence satisfaction evaluation model; and (2) defining one type of smart contract to create an interactive and efficient feedback-sharing method in a decentralized blockchain architecture, which could effectively achieve the goals of transparency and trustworthiness. This study provides a novel solution for understanding the residence satisfaction with living houses with trustworthiness consideration, inspiring more discussions about blockchain technology.