Reviewerly/exHarmony huggingface dataset:
Exploring topic modelling for generalising design requirements in complex design

AbstractAs the redesign process progresses in product lifecycle management, effectively managing engineering changes becomes increasingly challenging, often leading to catastrophic and costly project failures. In response, the study provides a framework for generalising design requirements documents into topics that engineers can use to understand complex designs. Based on previous work, this study employs and compares four different models, including latent Dirichlet allocation (LDA), the collapsed Gibbs sampling algorithm for the Dirichlet multinomial mixtures model (GSDMM), LDA-BERT, and GSDMM-BERT to determine the appropriate representation of requirements documents. Both heatmaps and UMAPs are used to illustrate the correlation between topics and words. The results indicate that the combined vector representation of topic modelling and the sentence-BERT model outperforms single topic modelling. This combined model leverages the additional knowledge from a pre-trained sentence-BERT model, thereby improving model performance and word distribution in all three industrial projects. Through this proposed framework, engineers can potentially generalise high-quality requirements topics for large requirements documents.KEYWORDS: Requirement managementrequirement topicscomplex designBERTdesign process Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 https://radimrehurek.com/gensim/models/ldamodel.html2 https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.pdist.html
Automated Assessment of Comprehension Strategies from Self-Explanations Using LLMs

Text comprehension is an essential skill in today’s information-rich world, and self-explanation practice helps students improve their understanding of complex texts. This study was centered on leveraging open-source Large Language Models (LLMs), specifically FLAN-T5, to automatically assess the comprehension strategies employed by readers while understanding Science, Technology, Engineering, and Mathematics (STEM) texts. The experiments relied on a corpus of three datasets (N = 11,833) with self-explanations annotated on 4 dimensions: 3 comprehension strategies (i.e., bridging, elaboration, and paraphrasing) and overall quality. Besides FLAN-T5, we also considered GPT3.5-turbo to establish a stronger baseline. Our experiments indicated that the performance improved with fine-tuning, having a larger LLM model, and providing examples via the prompt. Our best model considered a pretrained FLAN-T5 XXL model and obtained a weighted F1-score of 0.721, surpassing the 0.699 F1-score previously obtained using smaller models (i.e., RoBERTa).
Requirements for a processing-node quantum repeater on a real-world fiber grid

Abstract We numerically study the distribution of entanglement between the Dutch cities of Delft and Eindhoven realized with a processing-node quantum repeater and determine minimal hardware requirements for verifiable blind quantum computation using color centers and trapped ions. Our results are obtained considering restrictions imposed by a real-world fiber grid and using detailed hardware-specific models. By comparing our results to those we would obtain in idealized settings, we show that simplifications lead to a distorted picture of hardware demands, particularly on memory coherence and photon collection. We develop general machinery suitable for studying arbitrary processing-node repeater chains using NetSquid, a discrete-event simulator for quantum networks. This enables us to include time-dependent noise models and simulate repeater protocols with cut-offs, including the required classical control communication. We find minimal hardware requirements by solving an optimization problem using genetic algorithms on a high-performance-computing cluster. Our work provides guidance for further experimental progress, and showcases limitations of studying quantum-repeater requirements in idealized situations.
A global digital identity for all: the next evolution

This paper chronicals the emergence of digital identity as a legal concept, how digital identity has grown in importance at the national level over the past decades and is now poised to become even more important internationally. This work builds on existing scholarship, to consider the next evolution of digital identity from what is now essentially a national concept into a global, legal concept. The examination looks to the likely emergence of a global digital identity for individuals in the near future and asks how that could be achieved. The authors examine the use of blockchain technology as a possible foundation of a global digital identity, along with the necessary development of existing international law on individual rights to support a global digital identity for all. Blockchain is viewed as relatively more secure and it enables individuals to have more control over how their identity information is managed and used. Blockchain’s traceability provides advantages for government and the private sector in managing and verifying identity. It aids the integrity of identity information and related transactions. However, it is important to note that, while blockchain has advantages, its relative immutability can lead to the creation and use of false digital identities that cannot be easily detected or corrected. As this paper discusses, this aspect can undermine the integrity and reliability of digital identity nationally and internationally. Given that blockchain technology is fallible, the authors argue that international law has a vital role now and in the future in recognizing the right to digital identity and establishing norms of conduct.
Is Visual Explanation with Grad-CAM More Reliable for Deeper Neural Networks? A Case Study with Automatic Pneumothorax Diagnosis

While deep learning techniques have provided the state-of-the-art performance in various clinical tasks, explainability regarding their decision-making process can greatly enhance the credence of these methods for safer and quicker clinical adoption. With high flexibility, Gradient-weighted Class Activation Mapping (Grad-CAM) has been widely adopted to offer intuitive visual interpretation of various deep learning models' reasoning processes in computer-assisted diagnosis. However, despite the popularity of the technique, there is still a lack of systematic study on Grad-CAM's performance on different deep learning architectures. In this study, we investigate its robustness and effectiveness across different popular deep learning models, with a focus on the impact of the networks' depths and architecture types, by using a case study of automatic pneumothorax diagnosis in X-ray scans. Our results show that deeper neural networks do not necessarily contribute to a strong improvement of pneumothorax diagnosis accuracy, and the effectiveness of GradCAM also varies among different network architectures.
A Blockchain-based Conceptual Model to Address Educational Certificate Verification Challenges in Tanzania

The proliferation of counterfeit educational certificates is an ongoing issue around the world, including Tanzania. The effect of this malpractice is detrimental to the credibility of education. Traditional strategies to prevent fake certificates are abortive, calling for a more sophisticated approach. Blockchain technology has recently emerged as an ideal solution to this problem due to its inherent attributes that ensure disintermediation, immutability, tamper proof, anonymity, transparency, consensus, security, and trust. However, most existing blockchain-based solutions lack crucial functionalities that are pertinent to the Tanzanian education system. This study unveiled the challenges faced by the current verification system in Tanzania and proposed a blockchain-based conceptual model to address them. The proposed model is based on blockchain, smart contracts, and the Interplanetary File System (IPFS). Quantitative and qualitative methods were used to investigate certification problems in Tanzania and modeling techniques were used to construct the conceptual model. The findings showed that the main challenges of the current verification system emanate from manual procedures, unverifiable credentials, susceptibility of centralized storage systems, disintegrated verification systems, revocation problems, difficulties in communication, and high dependency on the issuers. These challenges undermine certificate verification, impose a significant setback in the fight against forgeries, and create loopholes. It was conceptually demonstrated that these issues can be resolved through the proposed blockchain-based solution.
Mayfly Optimization with Deep Learning-based Robust Object Detection and Classification on Surveillance Videos

Surveillance videos are recordings captured by video recording devices for monitoring and securing an area or property. These videos are frequently used in applications, involving law enforcement, security systems, retail analytics, and traffic monitoring. Surveillance videos can provide valuable visual information for analyzing patterns, identifying individuals or objects of interest, and detecting and investigating incidents. Object detection and classification on video surveillance involves the usage of computer vision techniques to identify and categorize objects within the video footage. Object detection algorithms are employed to locate and identify objects within each frame. These algorithms use various techniques, namely bounding box regression, Convolutional Neural Networks (CNNs), and feature extraction to detect objects of interest. This study presents the Mayfly Optimization with Deep Learning-based Robust Object Detection and Classification (MFODL-RODC) method on surveillance videos. The main aim of the MFODL-RODC technique lies in the accurate classification and recognition of objects in surveillance videos. To accomplish this, the MFODL-RODC method follows a two-step process, consisting of object detection and object classification. The MFODL-RODC method uses the EfficientDet object detector for the object detection process. Besides, the classification of detected objects takes place using the Variational Autoencoder (VAE) model. The MFO algorithm is employed to enrich the performance of the VAE model. The simulation examination of the MFODL-RODC technique is performed on benchmark datasets. The extensive results accentuated the improved performance of the MFODL-RODC method over other existing algorithms with an output of 98.89%.
A Detection and Investigation Model for the Capture and Analysis of Network Crimes

Investigation in the field of network forensics involves examining network traffic to identify, capture, preserve, reconstruct, analyze, and document network crimes. Although there are different perspectives on the practical and technical aspects of network forensics, there is still a lack of fundamental guidelines. This paper proposes a new detection and investigation model for capturing and analyzing network crimes, using design science research. The proposed model involves six processes: identification, verification, gathering, preservation, examination, analysis, and documentation. Each process is associated with several activities that provide the investigation team with a clear picture of exactly what needs to be performed. In addition, the proposed model has a unique activity, namely reporting. As a result, this model represents a comprehensive approach to network forensics investigations. It is designed to work in conjunction with established forensic techniques to ensure that forensic evidence from the network is collected and analyzed efficiently and effectively following accepted forensic procedures. The proposed model was compared with existing models in terms of completeness, showing that it is complete and can be adapted to any type of network and legal framework.
Deformable Cross-Attention Transformer for Medical Image Registration

Transformers have recently shown promise for medical image applications, leading to an increasing interest in developing such models for medical image registration. Recent advancements in designing registration Transformers have focused on using cross-attention (CA) to enable a more precise understanding of spatial correspondences between moving and fixed images. Here, we propose a novel CA mechanism that computes windowed attention using deformable windows. In contrast to existing CA mechanisms that require intensive computational complexity by either computing CA globally or locally with a fixed and expanded search window, the proposed deformable CA can selectively sample a diverse set of features over a large search window while maintaining low computational complexity. The proposed model was extensively evaluated on multi-modal, mono-modal, and atlas-to-patient registration tasks, demonstrating promising performance against state-of-the-art methods and indicating its effectiveness for medical image registration. The source code for this work is available at http://bit.ly/47HcEex .
Real-Time AI-Driven Fall Detection Method for Occupational Health and Safety

Fall accidents in industrial and construction environments require an immediate reaction, to provide first aid. Shortening the time between the fall and the relevant personnel being notified can significantly improve the safety and health of workers. Therefore, in this work, an IoT system for real-time fall detection is proposed, using the ASSIST-IoT reference architecture. Empowered with a machine learning model, the system can detect fall accidents and swiftly notify the occupational health and safety manager. To train the model, a novel multimodal fall detection dataset was collected from ten human participants and an anthropomorphic dummy, covering multiple types of fall, including falls from a height. The dataset includes absolute location and acceleration measurements from several IoT devices. Furthermore, a lightweight long short-term memory model is proposed for fall detection, capable of operating in an IoT environment with limited network bandwidth and hardware resources. The accuracy and F1-score of the model on the collected dataset were shown to exceed 0.95 and 0.9, respectively. The collected multimodal dataset was published under an open license, to facilitate future research on fall detection methods in occupational health and safety.
Chromatography Denoising with Improved Wavelet Thresholding Based on Modified Genetic Particle Swarm Optimization

The wavelet threshold functions are widely used in oil chromatography denoising because high-quality signals are the basis for Dissolved Gas Analysis (DGA), which determines the accuracy of transformer fault monitoring. However, there are certain limitations of the wavelet threshold functions, such as the Pseudo-Gibbs phenomenon and improper threshold selection. To this purpose, a modified genetic particle swarm optimization-based improved threshold function denoising method (MGPSO-ITF) is proposed. Specifically, the method constructs a new parametric threshold function that possesses high-order derivability and a small constant deviation. To obtain optimal values for the tunable parameters, MGPSO is employed, which outperforms other methods in identifying the optimum and achieving fast convergence. The simulation results demonstrate that the enhanced thresholding function yields a higher Signal-to-Noise Ratio (SNR), higher Noise Suppression Ratio (NSR), and smaller Root Mean Square Error (RMSE) compared to prior methods. Specifically, for the originally relatively smooth signal, MGPSO-ITF does not over-correct it to cause distortion. Furthermore, experiments on measured signals illustrate that the MGPSO-ITF is highly effective at denoising and preserving the original signal properties. Particularly in cases where peak deformation is prominent, the algorithm outperforms both hard and soft thresholding methods, achieving a reduction of 2.934% and 1.029% in peak area error, respectively.
Examining the influence of industry 4.0 in healthcare supply chain performance: Moderating role of environmental dynamism

Healthcare supply chain management (HCSCM) is a network of different systems, tools, and processes. This system works in a seamless manner to ensure healthcare related materials such as medicines, hospital supplies, and so on. These materials are manufactured in the firms, distributed through agents, and eventually reach patients on time. During COVID-19, HCSCM was severely impacted in many countries. Many patients had to face severe difficulties during the pandemic. But post pandemic and after the adoption of several modern technologies like Industry 4.0, the performance of HCSCM was more reliable. Not many studies are available which have examined the influence of Industry 4.0 in HCSCM during such a crisis. In such context, the study aims at examining the influence of Industry 4.0 in HCSCM with moderating role of environmental dynamism. A conceptual model is proposed with the support of theories and literature. This was later validated by PLS-SEM approach considering 312 respondents from people of HCSCM system. The study ascertained that Industry 4.0 has greatly improved the performance of HCSCM system after COVID-19 crisis. In addition, this study highlighted that environmental dynamism has a significant moderating role on the linkage between adoption of industry 4.0 technology with HCSCM.
Compositional data analysis of heavy metal contamination and eco-environmental risks in Himalayan agricultural soils, northern Pakistan

The importance of soil as a critical environmental medium cannot be overstated, as it provides various ecosystem services while supporting agricultural activities. Regrettably, soil contamination can impair the utility of these services and productivity in agriculture, and may even create several health hazards. Therefore, this study aimed to envisage the levels of heavy metals (HM) contamination in agricultural soils and identify the possible sources of contamination. A total of 54 soil samples from the designated area were obtained and subjected to analysis for the presence of nine HM, i.e., Ni, Cd, Cr, Co, Fe, Mn, Cu, Zn, and Pb, using atomic absorption spectrometry. This study used several single and multi-parameter indices for pollution and ecological risk evaluation. Compositional data analysis (CoDa) was used to assess the associations and potential sources of the HM. The findings revealed that Fe demonstrated the highest concentration among the studied HM, while Cd exhibited the lowest. The ecological risks were primarily attributed to Mn and Cd, with a considerable (40%) number of samples indicating high ecological risks. Based on contamination indices, Cd was identified as the study area's primary concern for soil pollution. Nevertheless, the PLI and modified degree of contamination (mCdeg) values exhibited a minimal inclusive effect of HM on soil. Furthermore, it was determined that the source of origin for all HM except Cd and Pb was geogenic. The soils in the area are under lesser human influence and mostly suggest a natural state. However, the contamination risk posed by Cd and the ecological risk posed by Cd and Mn raise concerns. A comprehensive evaluation of the contamination status and the risks was obtained using multiple indices. This study highlights the need for monitoring and managing HM contamination and mitigating the potential risks from Cd and Mn contamination in the area.
Variational quantum algorithm for node embedding

Quantum machine learning has made remarkable progress in many important tasks. However, the gate complexity of the initial state preparation is seldom considered in lots of quantum machine learning algorithms, making them non-end-to-end. Herein, we propose a quantum algorithm for the node embedding problem that maps a node graph's topological structure to embedding vectors. The resulting quantum embedding state can be used as an input for other quantum machine learning algorithms. With O(log(N)) qubits to store the information of N nodes, our algorithm will not lose quantum advantage for the subsequent quantum information processing. Moreover, owing to the use of a parameterized quantum circuit with O(poly(log(N))) depth, the resulting state can serve as an efficient quantum database. In addition, we explored the measurement complexity of the quantum node embedding algorithm, which is the main issue in training parameters, and extended the algorithm to capture high-order neighborhood information between nodes. Finally, we experimentally demonstrated our algorithm on an nuclear magnetic resonance quantum processor to solve a graph model.
Exploring incomplete decoupling modeling with window and cross-window mechanism for skeleton-based action recognition

Skeleton-based action recognition relies on capturing connections between joints to extract action-specific features. Current approaches utilizing temporal convolution for inter-frame joint relationships only capture connections within the same joint across different frames. It neglects potential connections between joints in different frames. However, modeling all joints across frames will encode a plethora of redundant features that impede the recognition of action. In this study, a novel and effective skeleton window attention mechanism (SWAM) is proposed that consists of window attention module (WAM) and spatial–temporal attention regularization (STAR). WAM directly capture short-range features within isolated windows. It consolidates all joints from adjacent frames into the window and performs full attention operations on them. STAR enhances the model's focus on structural action features, counteracting the vanilla attention mechanism's oversight. Furthermore, this study introduces the cross-window mechanism (CWM) that consists of three components: cross-window fusion (CWF), cross-window shift (CWS), and cross-window aggregation (CWA). These components collectively break the limitations imposed by window boundaries, enabling efficient interactions between windows and facilitating the capture of long-range dependencies. Integrating SWAM and CWM, we introduce the skeleton window attention network (SWA-Net), which embodies a novel approach of incomplete decoupling modeling. It can extract action features from different joints in different frames without capturing excessive redundant features. Compared with the state-of-the-art methods, it achieves better results on NTU-RGB+D, NTU-RGB+D 120 and Kinetics-Skeleton 400 datasets.
Deciphering the cryptocurrency conundrum: Investigating speculative characteristics and volatility

This study employs Time-Varying Parameter Vector Autoregression (TVP-VAR) to analyze spillover effects among ten financial instruments spanning 395 weeks, encompassing significant events such as the 2016/17 cryptocurrency surge, the COVID-19 pandemic, and the Ukraine conflict. The research highlights Bitcoin's influence on equities and commodities, influenced by evolving factors, regulatory uncertainties, and market sentiment. Equity indices both transmit and absorb shocks, while commodities intensify market volatility. Overall, Bitcoin notably impacts speculative assets such as equity indices, with a moderated influence on less speculative assets, emphasizing its pivotal role in dynamic financial markets subject to changing regulations.