Reviewerly/exHarmony huggingface dataset:
On the Viability of Diagrams and Drawings as System Requirements

Requirement expression media is an overlooked consideration in requirements innovation. The path to including media other than natural language requirement sentences begins with determining the viability of media to be used as requirement expressions in systems engineering. This research uses the INCOSE characteristics for individual requirements and sets of requirements as a basis for an assessment of the sufficiency of model diagrams and engineering drawings to express requirements. A critical review of high-impact sources is used to develop characteristic lists for natural language requirement sentences and for model diagrams and engineering drawings. A comparative analysis of the characteristic lists for requirement sentences, model diagrams, and engineering drawings shows that each have the same fundamental characteristics and the differences are based on the ability of the media to represent abstraction. The contribution of this research is the establishment of model diagrams and engineering drawings as viable alternatives to natural language sentences for system engineering requirements expressions.
Impact of informal institutions on youth agribusiness participation in Southern Benin

Abstract The agribusiness sector development is often portrayed as an essential component of economic development. Though Benin is a country with inestimable agricultural potential, the agribusiness sector appears unappealing to the local youths. Prior investigations diagnosed the impeding factors as a paucity of financial resource and a dearth of land and technical knowledge. This article departs from past studies by considering the importance of informal institutions for youth participation in the agribusiness sector. Exploring a rich data set of 478 youths aged 15 to 35, collected in Southern Benin based on the stratified random sampling technique, the article uses propensity score matching to address selection bias and logistic regression on the matched sample to link informal institutions to youth participation in agribusiness. Results highlight that agribusiness-unfriendly informal institutions discourage youth participation in the agribusiness sector. Though easing access to finance is crucial for the youths to initiate agribusiness activities, findings make a case for long-term policies that aim to increase the societal levels of trust and desirability for the agribusiness sector.
Surpassing the repeaterless bound with a photon-number encoded measurement-device-independent quantum key distribution protocol

Abstract Decoherence is detrimental to quantum key distribution (QKD) over large distances. One of the proposed solutions is to use quantum repeaters, which divide the total distance between the users into smaller segments to minimise the effects of the losses in the channel. Here we introduce a measurement-device-independent protocol which uses high-dimensional states prepared by two distant trusted parties and a coherent total photon number detection for the entanglement swapping measurement at the repeater station. We present an experimentally feasible protocol that can be implemented with current technology as the required states reduce down to the single-photon level over large distances. This protocol outperforms the existing measurement-device-independent and twin-field QKD protocols by achieving better key rates in general and higher transmission distance in total when experimental imperfections are considered. It also surpasses the fundamental limit of the repeaterless bound at a much shorter transmission distance in comparison to the existing TF-QKD protocols.
Full-Field Vibration Measurements by Using High-Speed Two-Dimensional Digital Image Correlation

This work developed a method that uses a single monochrome high-speed camera without sacrificing the spatial resolution to measure both in-plane and out-of-plane full-field vibrations. By using the high-speed camera and a two-dimensional digital image correlation (2D-DIC) algorithm, the method first extracts the out-of-plane displacement field from the measured virtual in-plane strains. Then it retrieves the in-plane displacement field after eliminating the out-of-plane motion-induced virtual component. For validation, in-plane and out-of-plane translation tests and single-frequency vibration experiments were carried out. The measurement results show good agreement with the reference values, indicating the effectiveness of the proposed high-speed 2D-DIC (HS-2D-DIC). Further, the natural frequencies and mode shapes of a rectangular cantilever panel were also measured successfully, exhibiting the method’s effectiveness in practical applications. Since the HS-2D-DIC requires only a single monochrome camera, no complex optical setup, and no complicated calibration process, the method can be developed as a competitive tool for full-field vibration characterizations.
Reclaiming the teacher perspective in digital education – an analysis of university teachers’ agency

Current approaches to digital technology in higher education teaching and learning often instrumentalise teaching and teachers in paving the way for technology. Reclaiming the teacher perspective, this article explores teachers’ agency in relation to technology in higher education. The aim is addressed through examining how teachers address the technological benefits for teaching. Analysing data from Danish university teachers (n = 488), we identify eight benefits of technology use underpinned by five value categories. The results show that teaching is multifaceted and that several factors contribute to the achievement of agency in digital teaching. The study provides a more substantial basis for making choices on digital education that align with teachers’ values and practices.
High‐precision skeleton‐based human repetitive action counting

Abstract A novel counting model is presented by the authors to estimate the number of repetitive actions in temporal 3D skeleton data. As per the authors’ knowledge, this is the first work of this kind using skeleton data for high‐precision repetitive action counting. Different from existing works on RGB video data, the authors’ model follows a bottom‐up pipeline to clip the sub‐action first followed by robust aggregation in inference. First, novel counting loss functions and robust inference with backtracking is proposed to pursue precise per‐frame count as well as overall count with boundary frames. Second, an efficient synthetic approach is proposed to augment skeleton data in training and thus avoid time‐consuming repetitive action data collection work. Finally, a challenging human repetitive action counting dataset named VSRep is collected with various types of action to evaluate the proposed model. Experiments demonstrate that the proposed counting model outperforms existing video‐based methods by a large margin in terms of accuracy in real‐time inference.
<scp>FBO‐RNN</scp>: Fuzzy butterfly optimization‐based <scp>RNN‐LSTM</scp> for extracting sentiments from Twitter Emoji database

Summary Social media networks have seen a significant increase in purpose and scale. Sentimental analysis is a component of social networking platforms that uses shared material to infer information about individual sentiments and emotions. In recent years, sentiment analysis (SA) research has grown in popularity. Twitter is the most popular social media platform, with users from many languages and cultures participating. Emojis are used by users to express themselves, and social media platforms contain a wide range of symbols, emotions, and opinions. A novel framework for SA based on Emojis is presented in this article. Initially, the noise‐free videos and images are filtered. The dictionary of Jieba was obtained by adding the English Emoji lexicon and English Internet slang lexicon to segment English text. Initially, the Emojis are converted into textual features. Different sentiment classes such as positive, very positive, neutral, negative, and very negative classes are classified using long short‐term memory (LSTM) in the recurrent neural network (RNN)‐based Fuzzy Butterfly Optimization (FBO) algorithm. The freeware WEKA software tool with different evaluation measures performs experimental investigations. Ultimately, the proposed model demonstrates superior results in the case of SA than other state‐of‐the‐art methods.
Multi-class classification based on quantum state discrimination

We present a general framework for the problem of multi-class classification using classification functions that can be interpreted as fuzzy sets. We specialize these functions in the domain of Quantum-inspired classifiers, which are based on quantum state discrimination techniques. In particular, we use unsharp observables (Positive Operator-Valued Measures) that are determined by the training set of a given dataset to construct these classification functions. We show that such classifiers can be tested on near-term quantum computers once these classification functions are “distilled” (on a classical platform) from the quantum encoding of a training dataset. We compare these experimental results with their theoretical counterparts and we pose some questions for future research.
Detecting backdoor in deep neural networks via intentional adversarial perturbations

Recent researches show that deep learning model is susceptible to backdoor attacks. Many defenses against backdoor attacks have been proposed. However, existing defense works require high computational overhead or backdoor attack information such as the trigger size, which is difficult to satisfy in realistic scenarios. In this paper, a novel backdoor detection method based on adversarial examples is proposed. The proposed method leverages intentional adversarial perturbations to detect whether an image contains a trigger, which can be applied in both the training stage and the inference stage (sanitize the training set in training stage and detect the backdoor instances in inference stage). Specifically, given an untrusted image, the adversarial perturbation is added to the image intentionally. If the prediction of the model on the perturbed image is consistent with that on the unperturbed image, the input image will be considered as a backdoor instance. Compared with most existing defense works, the proposed adversarial perturbation based method requires low computational resources and maintains the visual quality of the images. Experimental results show that, the backdoor detection rate of the proposed defense method is 99.63%, 99.76% and 99.91% on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively. Besides, the proposed method maintains the visual quality of the image as the l2 norm of the added perturbation are as low as 2.8715, 3.0513 and 2.4362 on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively. In addition, it is also demonstrated that the proposed method can achieve high defense performance against backdoor attacks under different attack settings (trigger transparency, trigger size and trigger pattern). Compared with the existing defense work (STRIP), the proposed method has better detection performance on all the three datasets, and is more efficient than STRIP.
Design, Manufacturing and Acoustic Assessment of Polymer Mouthpieces for Trombones

Brass instruments mouthpieces have been historically built using metal materials, usually brass. With the auge of additive manufacturing technologies new possibilities have arisen, both for testing alternative designs and for using new materials. This work assesses the use of polymers for manufacturing trombone mouthpieces, specifically PLA and Nylon. The acoustical behavior of these two mouthpieces has been compared with the obtained from a third one, built from brass. Both additive and subtractive manufacturing techniques were used, and the whole manufacturing process is described. The mouthpieces were acoustically assessed in an anechoic chamber with the collaboration of a professional performer. The harmonic analysis confirmed that all the manufactured mouthpieces respect the harmonic behavior of the instrument. An energy analysis of the harmonics revealed slight differences between the mouthpieces, which implies differences in the timbre of the instrument. Although these subtle differences would not be acceptable when performing with the instrument in an orchestra, they could be perfectly valid for early learners, personal rehearsals or any kind of alternative performance.
UAV Mission Path Planning Based on Reinforcement Learning in Dynamic Environment

With the rapid development of information technology, various products used in information technology are also constantly optimized. Among them, the task and path planning of UAV in the high-end robot industry has always been the focus of relevant researchers. In the high-end robot industry, in addition to the research and development of UAVs, they also continue to learn and strengthen the task and path planning of UAVs. Nowadays, using unmanned aerial vehicles for real-time shooting has become the trend of this era. Drones have brought great convenience to people’s lives, and more and more people are willing to use drones. Based on the above situation, this paper studies the task and path planning of UAV based on reinforcement learning in dynamic environment. In the case of unpredictable scene parameters, reinforcement learning method can be established by value function. Thus, a more reasonable path can be given to realize the reconnaissance and detection of points of interest. MATLAB simulation experiments show that the algorithm can effectively detect targets in complex terrain composed of terrain restricted areas, and return to the designated end point to complete communication. Firstly, the development of unmanned aerial vehicles in various countries and the social status of unmanned aerial vehicles are discussed. By making UAV build threat model and task allocation in dynamic environment. The path planning and optimization of UAV in dynamic environment is studied, and the path planning algorithm and Hungarian algorithm are added. The optimized UAV has the fastest data transmission and calculation speed, while the other two types of UAVs have slower data transmission and calculation speed. In particular, ordinary UAVs also have data transmission failures, resulting in incomplete experimental results. The results show that the optimized UAV system is better in data calculation and transmission, which also shows that the UAV can quickly plan and process flight paths, which is suitable for practical applications.
Is High-Fidelity Important for Human-like Virtual Avatars in Human Computer Interactions?

Survey/review study Is High-Fidelity Important for Human-like Virtual Avatars in Human Computer Interactions? Qiongdan Cao , Hui Yu ,*, Paul Charisse , Si Qiao , and Brett Stevens 1 University of Portsmouth, Portsmouth, PO1 2UP, UK * Correspondence: hui.yu@port.ac.uk Received: 2 March 2023 Accepted: 6 March 2023 Published: 27 March 2023 Abstract: As virtual avatars have become increasingly popular in recent years, current needs indicate that “interactivity” is crucial for inducing a positive response from users towards these avatars, especially in human computer interactions (HCI). This paper reviews recent works on high-fidelity human-like virtual avatars (e.g. high visual and motion fidelity) and discusses the critical question——“Is high-fidelity a positive choice for virtual avatars to achieve better interactions in HCI”. Furthermore, we summarise current technical approaches to developing those virtual avatars. We investigate the advantages and disadvantages of high-fidelity virtual avatars in different areas focusing on addressing the effect of motion, especially the upper body. Research shows that high-fidelity is a positive choice for virtual avatars, although it may depend on the application.
Adaptive Dimensional Gaussian Mutation of PSO-Optimized Convolutional Neural Network Hyperparameters

The configuration of the hyperparameters in convolutional neural networks (CNN) is crucial for determining their performance. However, traditional methods for hyperparameter configuration, such as grid searches and random searches, are time consuming and labor intensive. The optimization of CNN hyperparameters is a complex problem involving multiple local optima that poses a challenge for traditional particle swarm optimization (PSO) algorithms, which are prone to getting stuck in the local optima and achieving suboptimal results. To address the above issues, we proposed an adaptive dimensional Gaussian mutation PSO (ADGMPSO) to efficiently select the optimal hyperparameter configurations. The ADGMPSO algorithm utilized a cat chaos initialization strategy to generate an initial population with a more uniform distribution. It combined the sine-based inertia weights and an asynchronous change learning factor strategy to balance the global exploration and local exploitation capabilities. Finally, an elite particle adaptive dimensional Gaussian mutation strategy was proposed to improve the population diversity and convergence accuracy at the different stages of evolution. The performance of the proposed algorithm was compared to five other evolutionary algorithms, including PSO, BOA, WOA, SSA, and GWO, on ten benchmark test functions, and the results demonstrated the superiority of the proposed algorithm in terms of the optimal value, mean value, and standard deviation. The ADGMPSO algorithm was then applied to the hyperparameter optimization for the LeNet-5 and ResNet-18 network models. The results on the MNIST and CIFAR10 datasets showed that the proposed algorithm achieved a higher accuracy and generalization ability than the other optimization algorithms, such as PSO-CNN, LDWPSO-CNN, and GA-CNN.
Effect Of Geopolitical Risk On Bitcoin’s Price From 2016-2021: Comparative Analysis Pre-During Covid-19 Pandemic Period

Purpose of the study: The study aims to the effects of the geopolitical risks (GPR) and its components indexes on Bitcoin's price from January 1, 2016, to December 31, 2021, and how covid-19 affect the GPR risk and its components on bitcoin's price. Methodology: We performed quantitative data analysis techniques such as OLS (Ordinary Least Square) and QQ Regression. The study data covers January 1, 2016, to December 31, 2021. Main Findings: OLS estimations show that Bitcoin's prices are positively and negatively related and statistically insignificant to the GPR and its components before and during the COVID-19 pandemic, respectively. However, the findings of the Quantile estimations state that the effects are positive and statistically significant at the higher quantiles of bitcoin's prices of both the GPR and its component, i.e., GPRA before and during the COVID-19 pandemic. Research limitations/implications: Our paper has limitations related to the adopted methodology. We use bitcoin as the dependent variable and only use two estimation models: the OLS model and Quantile Regression. Novelty/Originality of this study: This research is expected to be used as a source of information and input for investors in deciding to invest in the face of another economic recession ahead. Our findings align with Bouri et al. (2017b) and Aysan et al. (2019), which illustrate that the effects of geopolitical risk and uncertainty indicators on Bitcoin's price are negative in the baseline findings. We enhance the previous findings to observe the positive impact of geopolitical risks and their components, the Bitcoin's price at the higher quantiles, then compare analysis pre-during Covid-19.
Statistical Analysis of Novel Ensemble Recursive Radial Basis Function Neural Network Performance on Global Solar Irradiance Forecasting

Reliable operation of energy management systems, grid stability, and managing energy demand responses are becoming challenging because of the flickering nature of solar irradiance. Accurate forecasting of global solar irradiance, i.e., global horizontal irradiance (GHI), plays a significant role in energy policy-making and the energy market. This paper proposes a novel global solar irradiance forecasting model based on the ensemble recursive radial basis function neural networks (ERRBFNNs). The various atmospheric inputs based on the built ensemble recursive radial basis function neural networks make the network more stable and robust to climatic uncertainty. This paper statistically investigates the performance of novel feed-forward neural networks based on forecasting models with various hidden nodes for global solar irradiance forecasting applications. We validated the proposed ERRBFNN global solar irradiance forecasting model using real-time data sets. The simulation results confirm that the proposed ensemble recursive radial basis function neural network based on global solar irradiance forecasting improves the accuracy, generalization, and network stability. Furthermore, the proposed ERRBFNN lowers the forecasting error to the least compared to other state-of-the-art forecasting models.
DIHBA: Dynamic, invisible and high attack success rate boundary backdoor attack with low poison ratio

Machine learning models are known to be vulnerable to malicious attacks, such as adversarial attacks, data poisoning attacks and backdoor attacks. A model injected by a backdoor attack can work adequately under normal conditions but the malicious behaviour is evident on samples with specific triggers. In most existing backdoor attacks, the trigger is manually defined, which results in a less-than-optimal attack success rate, a degree of stealthiness and a high poison ratio. To address these limitations, first, we reformulate the goal as a problem of determining the boundaries of a neural network. Then, we propose a dynamic invisible trigger algorithm (DIT) for determining the neural network's decision boundaries. Then, based on DIT, we propose DIHBA, Dynamic Invisible and High attack success rate with low poison ratio Boundaries Backdoor Attack, in which we use the decision boundary images generated by DIT as trigger images. Finally, we analyse the stealthiness, attack success rate and poison ratio of DIHBA with the MNIST, Fashion-MNIST, CIFAR10, GTSRB and Cat-face datasets. Compared to BadNets Gu et al.(2019), HB Saha et al.(2020), ROBNET Gong et al.(2021) and Poison Ink Zhang et al.(2022), DIHBA is not only stealthy but also has a high attack success rate, with low poison ratios of 0%, 0.1%, 0.5%, 20% and 25% for attacking different precision networks. In addition, DIHBA has strong resistance to the state-of-the-art backdoor defence systems Strip Gao et al.(2019) and Neural Cleanse Wang et al.(2019).
A service composition evolution method that combines deep clustering and a service requirement context model

Service composition can quickly build new value-added composite services by combining existing Web services. However, with the complex and changeable Internet environment, composite services need to effectively understand and manage requirements in a flexible and adaptive manner, and also be able to quickly and proactively provide high-quality services through a series of dynamic evolution models. In response to these challenges, in this study, we propose a service composition evolution method that can adaptively select the appropriate service to improve user satisfaction and service quality. First, we introduce a deep clustering method based on the topic model and auto-encoder, which considers the function description documents and parameter information of the services to reduce the search space of the candidate services. Furthermore, we describe a requirement-oriented context-sensitive task model to integrate functional and non-functional user requirements by connecting the context of Web subtasks. Then, we present a dynamic service matching method with QoS threshold judgement to filter and rank the services. We applied the proposed evolution method to the datasets of real-world Web services and measured the performance using standard measurement metrics. The prototype implementation and results of simulation experiments verified the effectiveness of each part of our method.