Reviewerly/exHarmony huggingface dataset:
An Identification Method for Irregular Components Related to Terminal Blocks in Equipment Cabinet of Power Substation

Despite the continuous advancement of intelligent power substations, the terminal block components within equipment cabinet inspection work still often require loads of personnel. The repetitive documentary works not only lack efficiency but are also susceptible to inaccuracies introduced by substation personnel. To resolve the problem of lengthy, time-consuming inspections, a terminal block component detection and identification method is presented in this paper. The identification method is a multi-stage system that incorporates a streamlined version of You Only Look Once version 7 (YOLOv7), a fusion of YOLOv7 and differential binarization (DB), and the utilization of PaddleOCR. Firstly, the YOLOv7 Area-Oriented (YOLOv7-AO) model is developed to precisely locate the complete region of terminal blocks within substation scene images. The compact area extraction model rapidly cuts out the valid proportion of the input image. Furthermore, the DB segmentation head is integrated into the YOLOv7 model to effectively handle the densely arranged, irregularly shaped block components. To detect all the components within a target electrical cabinet of substation equipment, the YOLOv7 model with a differential binarization attention head (YOLOv7-DBAH) is proposed, integrating spatial and channel attention mechanisms. Finally, a general OCR algorithm is applied to the cropped-out instances after image distortion to match and record the component's identity information. The experimental results show that the YOLOv7-AO model reaches high detection accuracy with good portability, gaining 4.45 times faster running speed. Moreover, the terminal block component detection results show that the YOLOv7-DBAH model achieves the highest evaluation metrics, increasing the F1-score from 0.83 to 0.89 and boosting the precision to over 0.91. The proposed method achieves the goal of terminal block component identification and can be applied in practical situations.
A Multi-Qubit Quantum Gate Using the Zeno Effect

The Zeno effect, in which repeated observation freezes the dynamics of a quantum system, stands as an iconic oddity of quantum mechanics. When a measurement is unable to distinguish between states in a subspace, the dynamics within that subspace can be profoundly altered, leading to non-trivial behavior. Here we show that such a measurement can turn a non-interacting system with only single-qubit control into a two- or multi-qubit entangling gate, which we call a Zeno gate. The gate works by imparting a geometric phase on the system, conditioned on it lying within a particular nonlocal subspace. We derive simple closed-form expressions for the gate fidelity under a number of non-idealities and show that the gate is viable for implementation in circuit and cavity QED systems. More specifically, we illustrate the functioning of the gate via dispersive readout in both the Markovian and non-Markovian readout regimes, and derive conditions for longitudinal readout to ideally realize the gate.
Arbitrary bias control of LiNbO3 based Mach-Zehnder intensity modulators for QKD system

Abstract Quantum key distribution (QKD) can help distant agents to share unconditional secret keys, and the achievable secret key rate can be enhanced with the help of decoy-state protocol. To implement QKD experimentally, the agents are supposed to accurately transmit a number of different intensity pulses with the LiNbO 3 based Mach-Zehnder (LNMZ) intensity modulator. However, the bias drift of LNMZ intensity modulator may affect the performance of a QKD system. In this letter, we reveal a simple RC circuit model to demonstrate the bias drift in the LNMZ intensity modulator. And based on the model, we propose a multi-step bias stable scheme to control the bias working point. Experimental result shows that our scheme can eliminate the bias drift of at arbitrary working point within a long time range. Besides, there is no need of any feedback mechanisms in the scheme. This means our scheme will not lead to any increasement in system complexity, making it more suitable for a QKD system.
Role of authentication factors in Fin-tech mobile transaction security

Abstract Fin-Tech is the merging of finance and technology, to be considered a key term for technology-based financial operations and money transactions as far as Fin-Tech is concerned. In the massive field of business, mobile money transaction security is a great challenge for researchers. The user authentication schemes restrict the ability to enforce the authentication before the account can access and operate. Although authentication factors provide greater security than a simple static password, financial transactions have potential drawbacks because cybercrime expands the opportunities for fraudsters. The most common enterprise challenge is mobile-based user authentication during transactions, which addresses the security issues against fraudsters. The confirmation of a user legitimation before the money transaction is highlighted by mechanisms and technologies from previous studies that may be helpful in user authentication. This study aims to identify the technologies for user authentication and the opportunity for their transformation to mobile money transaction security despite having all the legally required data for a transaction. This proposed review has identified the role of multifactor authentication techniques for authentication to mitigate the risk of fraudulent transactions—the analysis through 92 articles taken from famous publishers. The most relevant articles address authentication problems, of which 54 percent describe money transaction security, and the rest highlight the supporting technology for user authentication. The study platform described the technology-based approaches with the appreciation of new ideas for secure money transactions. The QR code and multifactor authentication are key terms that increased security by 46%. In addition, this also ensures the user is legitimate using advanced technologies and algorithms to predict and discover transaction risks and discourage fraudsters from trying.
Investigating the Impact of Task-Oriented, Relationship-Oriented, and Innovation-Oriented Leadership Competencies on Project Success in Pakistan: A Moderated Model of Multi-Dimensional Senior Management Support.

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Reflecting on Maker Education as a Potential Context for the Development of Spatial Ability

Based on significant evidence of the benefit of increasing learners’ levels of spatial ability, its development has become a focal agenda for many educators, educational researchers, and educational policy makers. While to date spatial ability has received little attention in research surrounding maker environments, maker education has the potential to be a particularly auspicious context for its development within learners. In this chapter, the seven case studies of maker education in this edited volume from Kenya, Mexico, China, the Netherlands (×2), Denmark, and South Korea are reflected upon through this specific lens. Each case study provoked a different dimension to this reflection, with questions of how spatial ability relates to “making”, how it’s development should be prioritised against other already noted aims of maker education, and what could be the implications of making spatial ability development an explicit goal of maker education inductively arising. These questions are unpacked, and it is envisioned that this reflection will underpin ensuing advances on if and how spatial ability development should be further examined or integrated into maker education environments.
Optimized Refactoring Mechanisms to Improve Quality Characteristics in Object-Oriented Systems

Refactoring has emerged as a predominant approach to augmenting software product quality. However, empirical evidence suggests that not all dimensions of software quality experience unending enhancements through refactoring. Current scholarly explorations reveal significant variances in the impacts of diverse refactoring methods, with potential adverse effects and contradictions surfacing concerning software quality. Consequently, such disparities render the advantages of refactoring contentious, culminating in challenges for software developers in the selection of optimal refactoring methods to ameliorate software quality. Existing literature lacks an in-depth exploration of the reasons behind the contrasting impacts of refactoring methods on quality enhancement or the development of refined protocols for employing these techniques. Therefore, this research aims to explore, identify, and fine-tune the utilization mechanisms of refactoring methods, empowering software developers to make informed choices for the enhancement of object-oriented systems’ quality attributes. Ten commonly employed refactoring methods were singled out for this investigation, each executed independently across five case studies varying in scale (small, medium, and large). The Quality Model for Object-Oriented Design (QMOOD) was employed as the evaluation tool to ascertain the influence of refactoring techniques on quality attributes. The research outcomes denote that the multifarious impacts of refactoring methods on quality attributes are attributed to distinct usage mechanisms of the techniques. These insights assist software practitioners in discerning the optimal utilization of refactoring methods to ameliorate software quality, taking their mechanisms into account. Moreover, these outcomes furnish industry experts with prescriptive guidelines for employing refactoring methods to elevate the quality of object-oriented systems, predicated on the suitable mechanism.
GraRep++: Flexible Learning Graph Representations With Weighted Global Structural Information

The key to vertex embedding is to learn low-dimensional representations of global graph information, and integrating information from multiple steps is an effective strategy. Existing research shows that the transition probability of each step can capture the relationship between different hops, and the graph’s global information can be obtained simultaneously. However, much of the current work simply concatenates representations of different hops into a global representation. In other words, they are unclear about the contribution of each <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -step ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k\geq {1}$ </tex-math></inline-formula> ) structural information in the embedding. With this motivation, we propose a unified framework that focuses on considering the contributions of different steps in global graph representation. It reconsiders the contribution of different steps in the global representation from two perspectives: (i)We flexibly assign different weights to the different steps loss function, (ii) According to different <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> , we design strategies that all <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -step representations are concatenated with different proportions to form a global representation. Based on this, we more effectively integrate global structural information into the learning process. In this paper, our proposed framework achieves competitive performance on vertex classification, link prediction, and visualization tasks on multiple datasets.
Prostate Segmentation in MRI Using Transformer Encoder and Decoder Framework

To develop an accurate segmentation model for the prostate and lesion area to help clinicians diagnose diseases, we propose a multi-encoder and decoder segmentation network, denoted Muled-Net, which can concurrently segment the prostate and lesion regions in an image. The model performs parallel calculations for dual input. In two encoder branches of the model, a new transformer encoder is used to overcome the fact that only information from the neighborhood pixels can be captured, increasing the ability to capture global dependencies. Furthermore, given the usually small size of the lesion, ASPP and feature fusion are merged to expand the perceptual field and retain more contextual information of the shallow layer in decoder. To the best of our limited knowledge, there is no public dataset for the segmentation of the prostate and its lesion regions. So we made a publicly usable dataset. Muled-Net is compared with other deep learning methods, FCN, U-Net, U-Net++, and ResU-Net with four-fold cross-validation. Of all 218 subjects, 140 healthy individuals and 78 patients with prostate cancer were included in this work. Average Dice of 95%, Iou of 89%, sensitivity of 94%, 95HD of 9.56, and MSD of 0.66 are achieved for the prostate segmentation and average Dice of 89%, Iou of 82%, sensitivity of 92%, 95HD of 11.16, and MSD of 1.09 for the segmentation of the prostate lesion regions. The performance of the proposed model has made significant improvements to the segmentation of the lesion regions in particular, suggesting that the model could be considered as an auxiliary tool to ease the workload of physicians and help them in making treatment decisions.
Stochastic optimization algorithms for quantum applications

Hybrid classical quantum optimization methods have become an important tool for efficiently solving problems in the current generation of noisy intermediate-scale quantum computers. These methods use an optimization algorithm executed in a classical computer, fed with values of the objective function obtained in a quantum processor. A proper choice of optimization algorithm is essential to achieve good performance. Here, we review the use of first-order, second-order, and quantum natural gradient stochastic optimization methods, which are defined in the field of real numbers, and propose stochastic algorithms defined in the field of complex numbers. The performance of all methods is evaluated by means of their application to variational quantum eigensolver, quantum control of quantum states, and quantum state estimation. In general, complex number optimization algorithms perform best, with first-order complex algorithms consistently achieving the best performance, closely followed by complex quantum natural algorithms, which do not require expensive hyperparameter calibration. In particular, the scalar formulation of the complex quantum natural algorithm allows to achieve good performance with low classical computational cost.
Software Subclassification Based on BERTopic-BERT-BiLSTM Model

With the continuous influx of application software onto the application software market, achieving accurate software recommendations for users in the huge software application market is urgent. To address this issue, each application software market currently provides its own classification tags. However, several problems still exist, such as the lack of objectivity, hierarchy, and standardization in these classifications, which in turn affects the accuracy of precise software recommendations. Accordingly, a customized BERTopic model is proposed to cluster the software description texts of the application software and the automatic tagging and updating of the application software tags are realized according to the clusters obtained by topic clustering and the extracted subject words. At the same time, a data enhancement method based on the c-TF-IDF algorithm is proposed to solve the problem of imbalance of datasets, and then the classification model based on the BERT-BiLSTM model is trained on the labeled datasets to classify the software in the dimension of the application function, so as to realize the accurate software recommendation for users. Based on the experimental verification of two datasets, 21 categories in the SourceForge dataset and 19 categories in the Chinese App Store dataset are subclassed by the clustering results of the customized BERTopic model, and the tags of 138 subclasses and 262 subclasses are formed, respectively. In addition, a complete tagged software description text dataset is constructed and the software tags are updated automatically. In the first stage of the classification experiment, the weighted average accuracy, recall rate, and F1 value can reach 0.92, 0.91, and 0.92, respectively. In the second stage, the weighted average accuracy, recall rate, and F1 value can all reach 0.96. After data enhancement, the weighted average F1 value of the classification model can be increased by up to two percentage points.
Sparks of Artificial General Recommender (AGR): Experiments with ChatGPT

This study investigates the feasibility of developing an Artificial General Recommender (AGR), facilitated by recent advancements in Large Language Models (LLMs). An AGR comprises both conversationality and universality to engage in natural dialogues and generate recommendations across various domains. We propose ten fundamental principles that an AGR should adhere to, each with its corresponding testing protocol. We proceed to assess whether ChatGPT, a sophisticated LLM, can comply with the proposed principles by engaging in recommendation-oriented dialogues with the model while observing its behavior. Our findings demonstrate the potential for ChatGPT to serve as an AGR, though several limitations and areas for improvement are identified.
Indoor Scene Recognition: An Attention-Based Approach Using Feature Selection-Based Transfer Learning and Deep Liquid State Machine

Scene understanding is one of the most challenging areas of research in the fields of robotics and computer vision. Recognising indoor scenes is one of the research applications in the category of scene understanding that has gained attention in recent years. Recent developments in deep learning and transfer learning approaches have attracted huge attention in addressing this challenging area. In our work, we have proposed a fine-tuned deep transfer learning approach using DenseNet201 for feature extraction and a deep Liquid State Machine model as the classifier in order to develop a model for recognising and understanding indoor scenes. We have included fuzzy colour stacking techniques, colour-based segmentation, and an adaptive World Cup optimisation algorithm to improve the performance of our deep model. Our proposed model would dedicatedly assist the visually impaired and blind to navigate in the indoor environment and completely integrate into their day-to-day activities. Our proposed work was implemented on the NYU depth dataset and attained an accuracy of 96% for classifying the indoor scenes.
A new hybrid risk assessment process for cyber security design of smart grids using fuzzy analytic hierarchy processes

Abstract IT vulnerabilities, cyber threats, and resulting risks significantly impact the stability of current and future power grids. The results of a Risk Assessment process contribute to a better understanding of the causes and nature of the associated risks. The risks assessed by experts are available in both numerical and linguistic representations – this makes it beneficial to include a combination of linguistic and numerical analyses. In this paper, we propose a new Hybrid Risk Assessment method based on fuzzy logic, leading to more precise results. The presented approach specifies the variables and membership functions of fuzzy logic with reference to Smart Grids. For this propose, a case study with five risk events in a small-scale Smart Grid is carried out as an example. The results can then support decision-makers in ensuring grid stability.
Fundamentalisation of professional training of future teachers of mathematics and computer science in the conditions of digitalisation

Relevance. Teachers of mathematics and computer science play an important role in shaping the fundamental knowledge of students. This knowledge manifests itself in the form of in-depth theoretical knowledge, readiness to apply it in practice, critical attitude to information, creativity in their actions. In the conditions of digital transformation, fundamental knowledge enables an individual to take an active part in the life of the modern society and generate new decisions in unfamiliar situations. The aim of the article is to identify due strategies for professional training of mathematics and computer science teachers in the conditions of digitalisation of education, those who are ready to implement the educational process at school with a focus on transfer of fundamental knowledge. Methodology and methods. The methodological basis of the research involves: systemic approach in defining the new role of the teacher as a subject of digital educational ecosystems; environmental approach in characterising the potential of digital educational environment; activity-based approach in training future teachers of mathematics and computer science towards enhancing their ability to form a system of fundamental knowledge in students. The results of the study are presented by describing digitalisation as a modern trend towards modernisation of education, defining the essence of fundamentalisation of education, establishing the interrelation between these processes. The priorities of digital educational space helping to improve the work of mathematics and computer science teachers aimed at the formation of students’ system of fundamental knowledge, to make it more efficient, rational, dynamic and creative, include: continuous online support of educational process, availability of high-quality verified and variable educational content, personalisation of educational trajectory of each student, operational feedback and monitoring of learners’ academic achievements, flexibility of educational programmes, due potential to create sustainable motivation, support of students’ involvement in active learning through the use of new educational tools (virtual simulators and laboratories), interactivity, multimediality and hypertextuality of the environment, automation of teacher’s routine activities, etc. Conclusion. Fundamentalisation of education is viewed as a process of formation of solid, profound basic knowledge defining a person’s scientific worldview and allowing for integration of this knowledge through interdisciplinary links, enabling a person to continuously engage in self-education. The presented results can be used for the development of educational programmes of pedagogical baccalaureate, programmes of supplementary professional education providing for due training of teachers of mathematics and computer science, development of their readiness to form a system of fundamental knowledge in students relying of the digital learning environment resource.
COVID-19: An Anaesthetic Moment

The COVID-19 pandemic was the definite point to understand an anaesthetic moment (or social numbness), an effect of neoliberalism and neoliberal polity (NNP). The COVID-19 pandemic compels us to reread the NNP to comprehend its consequences beyond the economic sphere. The arrival of an anaesthetic moment was a painful experience wherein social numbness towards marginalised sections was ubiquitous. The challenge and negotiation during the pandemic had quadrupled due to the construal of social pain as normalisation, violence as the new normal, deaths qua number, vulnerability as negative paternalisation and fait accompli, and absenting the role of critique.