Reviewerly/exHarmony huggingface dataset:
Exploring ab initio machine synthesis of quantum circuits

Abstract Gate-level quantum circuits are often derived manually from higher level algorithms. While this suffices for small implementations and demonstrations, ultimately automatic circuit design will be required to realise complex algorithms using hardware-specific operations and connectivity. Therefore, ab initio creation of circuits within a machine, either a classical computer or a hybrid quantum–classical device, is of key importance. We explore a range of established and novel techniques for the synthesis of new circuit structures, the optimisation of parameterised circuits, and the efficient removal of low-value gates via the quantum geometric tensor. Using these techniques we tackle the tasks of automatic encoding of unitary processes and translation (recompilation) of a circuit from one form to another. Using emulated quantum computers with various noise-free gate sets we provide simple examples involving up to 10 qubits, corresponding to 20 qubits in the augmented space we use. Further applications of specific relevance to chemistry modelling are considered in a sister paper, ‘Exploiting subspace constraints and ab initio variational methods for quantum chemistry’. The emulation environments used were QuEST , QuESTlink and pyQuEST . All resources will be made openly accessible and are currently available upon request.
The Impact of Quality Costs as a Mediator in the Relationship Between Management Accounting Systems and Financial Performance: the Case of Jordan

Objective: The current analysis aims to assess the mediating effect of cost quality on the relationship between management accounting systems and the financial performance of industrial enterprises listed on the Amman Stock Exchange. Theoretical framework: Three questionnaires are used for this purpose, their validity and reliability are checked; the first part deals with the cost of quality and consists of 15 paragraphs. The second part deals with the management accounting system and consists of 30 parts distributed in three dimensions (just in time, value chain, target cost). Design/methodology/approach: The third section is related to financial performance and consists of 10 paragraphs. It works on a random sample of 311 people. Results: The results show a statistically significant mediating role of quality cost on the relationship between the management accounting system and its three dimensions (just-in-time manufacturing, value chain, and target cost) and the financial performance of industrial companies listed on the Amman Stock Exchange. Originality: This study expands the use of data science techniques, big data, and artificial intelligence, as these are necessary and effective tools for improving the quality of financial performance and make a significant contribution to the fundamental objective.
The Metaverse for Cognitive Health: A Paradigm Shift

The Metaverse, frequently dubbed as ”the successor of mobile internet”, is a paradigm which aims to build an immersive, spatiotemporal and self-sustained virtual, but realistic world for all age groups. The Metaverse is stepping from science fiction to reality due to the advances in technologies like Extended Reality, Virtual Reality, Augmented Reality, Artificial Intelligence, the Internet of Everything, eXtended meta-uni-omni-Verse and Digital Twins. Consumer electronics such as wearables, television, and smartphones are essential components of the Metaverse that can be used to accurately collect and measure data concerning user interaction and thereby create an immersive environment. This article discusses the state of art literature about how the Metaverse can be used for improving cognitive health, various scenarios where the Metaverse could help doctors to better treat cognitive patients, and finally, the future research directions that can realize the potential of the Metaverse for cognitive health.
Best Architecture Recommendations of ANN Backpropagation Based on Combination of Learning Rate, Momentum, and Number of Hidden Layers

This article discusses the results of research on the combination of learning rate values, momentum, and the number of neurons in the hidden layer of the ANN Backpropagation (ANN-BP) architecture using meta-analysis. This study aims to find out the most recommended values at each learning rate and momentum interval, namely [0.1], as well as the number of neurons in the hidden layer used during the data training process. We conducted a meta-analysis of the use of learning rate, momentum, and number of neurons in the hidden layer of ANN-BP. The eligibility data criteria of 63 data include a learning rate of 44 complete data, the momentum of 30 complete data, and the number of neurons in the hidden layer of 45 complete data. The results of the data analysis showed that the learning rate value was recommended at intervals of 0.1-0.2 with a RE model value of 0.938 (very high), the momentum at intervals of 0.7-0.9 with RE model values of 0.925 (very high), and the number of neurons in the input layer that was smaller than the number of neurons in the hidden layer with a RE model value of 0.932 (very high). This recommendation is obtained from the results of data analysis using JASP by looking at the effect size of the accuracy level of research sample data.
A Novel Variant of the Salp Swarm Algorithm for Engineering Optimization

Abstract There are many design problems need to be optimized in various fields of engineering, and most of them belong to the NP-hard problem. The meta-heuristic algorithm is one kind of optimization method and provides an effective way to solve the NP-hard problem. Salp swarm algorithm (SSA) is a nature-inspired algorithm that mimics and mathematically models the behavior of slap swarm in nature. However, similar to most of the meta-heuristic algorithms, the traditional SSA has some shortcomings, such as entrapment in local optima. In this paper, the three main strategies are adopted to strengthen the basic SSA, including chaos theory, sine-cosine mechanism and the principle of quantum computation. Therefore, the SSA variant is proposed in this research, namely SCQ-SSA. The representative benchmark functions are employed to test the performances of the algorithms. The SCQ-SSA are compared with the seven algorithms in high-dimensional functions (1000 dimensions), seven SSA variants and six advanced variants on benchmark functions, the experiment reveals that the SCQ-SSA enhances resulting precision and alleviates local optimal problems. Besides, the SCQ-SSA is applied to resolve three classical engineering problems: tubular column design problem, tension/compression spring design problem and pressure vessel design problem. The design results indicate that these engineering problems are optimized with high accuracy and superiority by the improved SSA. The source code is available in the URL: https://github.com/ye-zero/SCQSSA/tree/main/SCQ-SSA .
Fractal Patterns in Groundwater Radon Disturbances Prior to the Great &lt;em&gt;M&lt;sub&gt;w&lt;/sub&gt;&lt;/em&gt;=7.9 Wenchuan Earthquake, China

This study reports fractal analysis of one-year radon in groundwater disturbances from five stations in China amid the catastrophic Wenchuan (Mw=7.9) earthquake of May 12, 2008 (day 133). Five techniques are used (DFA, fractal dimensions with Higuchi, Katz, Sevcik methods, power-law analysis) in segmented portions glided throughout each signal. Noteworthy fractal areas are outlined in the KDS, GS, MSS station data, while the portions were non-significant for PZHS and SPS. Up to day 133 critical epoch DFA-exponents have 1.5&amp;lt;=a&amp;lt;2.0 with several above 1.8. Fractal dimensions exhibit Katz’s D around 1.0-1.2, Higuchi’s D between 1.5-2.0 and Sevcik’s D between 1.0-1.5. Several power-law exponents are above 1.7 and numerous above 2.0. All fractal results of KDS-GS-MSS stations are further analysed using a novel computerised methodology which locates the exact out-of-threshold fractal areas and combines the outcomes of different methods per five, four, three, and two (maximum 13 combinations) versus nineteen Mw&amp;lt;=5.5 earthquakes of the area. Most different-technique coincidences are before the great Wenchuan earthquake and its post-earthquakes. This is not only with one method, but with 13 different methods. Other interpretations are also discussed.
An overview of the consumer‐centric disruptive technology research: Insights from topic modelling and literature review

Abstract This review aims to assess the most prevalent disruptive technologies of the past decade and evaluate them from a dual perspective. This study utilises the technology life cycle framework to classify disruptive technologies into three phases, (i) emerging, (ii) industry, and (iii) customer‐focused research outcomes. Subsequently, entities using six thematic categories via topic modelling are assessed to determine the research trajectory about customers, industries, and theory. The present study utilises the Latent Dirichlet Allocation framework for topic modelling, a quantitative tool that employs advanced statistical methodology to extract semantic knowledge from large text corpora. This approach offers a more comprehensive and insightful categorisation of information compared to traditional research review models such as archive review, meta‐review analysis, and systematic literature review. After the review, over 750 articles were analysed and classified according to their unique themes and technological emphasis. The study suggests practical applications that could enhance industrial progress through disruptive technology. Furthermore, by identifying gaps, the authors suggest multiple research prospects for forthcoming investigations on disruptive technologies and their impact on consumer behaviour. This research is novel in various aspects. This research is by far the only research record in the last decade that provides a systematic literature review of disruptive technologies with an extensive focus on customers. Considering the methodological perspective, this research uses topic modelling as a new literature review model, enabling analysing more research outputs and yielding more valuable findings than traditional methodologies.
Breast Cancer Diagnosis in Mammography Images Using Deep Convolutional Neural Network-Based Transfer and Scratch Learning Approach

Objectives:The study aims to utilize a Deep Convolutional Neural Network (Deep-CNN) model called MobileNetV2, which has low computational requirements, to accomplish binary classification of mammography images.To achieve this objective, the study investigates two methods: transfer learning and scratch learning.Methods: The proposed technique aims to classify mammography images from the Digital Database of Screening Mammography (DDSM) dataset into either malignant or benign categories using transfer learning and scratch learning methods based on Deep-CNN.Before being fed into the Deep-CNN, the images' contrast level is enhanced using the min-max contrast enhancement technique.MobileNetV2, a lightweight CNN architecture, is used as a convolutional base with three transfer learning variants, including transfer learning without fine-tuning, transfer learning with fine-tuning, and fixed feature extraction, to achieve binary classification.The study also attempts to develop a seven-layered CNN architecture to accomplish mammography image classification through scratch learning.The trained breast cancer detection models' classification performance, obtained through transfer and scratch learning methods, is evaluated based on machine learning performance metrics such as accuracy, precision, F1 Score, and area under the curve.Findings: The study conducted three transfer learning variants and found that combining MobileNetV2 with Random Forest classifier using the fixed feature extraction approach produced the best results, with an accuracy of 0.994 and a shorter training time of only 63.87 seconds compared to the other transfer learning variants.On the other hand, a seven-layer CNN model developed using scratch learning achieved a classification accuracy of 0.96, but it required a longer training period of 7980 seconds.Novelty: The breast cancer detection model introduced in this study exhibited superior performance compared to recently developed breast cancer detection models, as measured by accu-Bokade & Shah / Indian Journal of Science and Technology 2023;16(18):1385-1394 racy and Area Under the Curve (AUC).The study found that the model created using the fixed feature extraction method showcases the effectiveness of utilizing MobilenetV2 and a Random Forest machine learning classifier in decreasing the number of trainable network parameters and network training time.Consequently, this presents an advantage for implementing the model on low-cost embedded platforms with limited memory size.
Knowledge-Bridged Causal Interaction Network for Causal Emotion Entailment

Causal Emotion Entailment aims to identify causal utterances that are responsible for the target utterance with a non-neutral emotion in conversations. Previous works are limited in thorough understanding of the conversational context and accurate reasoning of the emotion cause. To this end, we propose Knowledge-Bridged Causal Interaction Network (KBCIN) with commonsense knowledge (CSK) leveraged as three bridges. Specifically, we construct a conversational graph for each conversation and leverage the event-centered CSK as the semantics-level bridge (S-bridge) to capture the deep inter-utterance dependencies in the conversational context via the CSK-Enhanced Graph Attention module. Moreover, social-interaction CSK serves as emotion-level bridge (E-bridge) and action-level bridge (A-bridge) to connect candidate utterances with the target one, which provides explicit causal clues for the Emotional Interaction module and Actional Interaction module to reason the target emotion. Experimental results show that our model achieves better performance over most baseline models. Our source code is publicly available at https://github.com/circle-hit/KBCIN.
Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information Principles

Role-based learning is a promising approach to improving the performance of Multi-Agent Reinforcement Learning (MARL). Nevertheless, without manual assistance, current role-based methods cannot guarantee stably discovering a set of roles to effectively decompose a complex task, as they assume either a predefined role structure or practical experience for selecting hyperparameters. In this article, we propose a mathematical Structural Information principles-based Role Discovery method, namely SIRD, and then present a SIRD optimizing MARL framework, namely SR-MARL, for multi-agent collaboration. The SIRD transforms role discovery into a hierarchical action space clustering. Specifically, the SIRD consists of structuralization, sparsification, and optimization modules, where an optimal encoding tree is generated to perform abstracting to discover roles. The SIRD is agnostic to specific MARL algorithms and flexibly integrated with various value function factorization approaches. Empirical evaluations on the StarCraft II micromanagement benchmark demonstrate that, compared with state-of-the-art MARL algorithms, the SR-MARL framework improves the average test win rate by 0.17%, 6.08%, and 3.24%, and reduces the deviation by 16.67%, 30.80%, and 66.30%, under easy, hard, and super hard scenarios.
Non-IID Transfer Learning on Graphs

Transfer learning refers to the transfer of knowledge or information from a relevant source domain to a target domain. However, most existing transfer learning theories and algorithms focus on IID tasks, where the source/target samples are assumed to be independent and identically distributed. Very little effort is devoted to theoretically studying the knowledge transferability on non-IID tasks, e.g., cross-network mining. To bridge the gap, in this paper, we propose rigorous generalization bounds and algorithms for cross-network transfer learning from a source graph to a target graph. The crucial idea is to characterize the cross-network knowledge transferability from the perspective of the Weisfeiler-Lehman graph isomorphism test. To this end, we propose a novel Graph Subtree Discrepancy to measure the graph distribution shift between source and target graphs. Then the generalization error bounds on cross-network transfer learning, including both cross-network node classification and link prediction tasks, can be derived in terms of the source knowledge and the Graph Subtree Discrepancy across domains. This thereby motivates us to propose a generic graph adaptive network (GRADE) to minimize the distribution shift between source and target graphs for cross-network transfer learning. Experimental results verify the effectiveness and efficiency of our GRADE framework on both cross-network node classification and cross-domain recommendation tasks.
Graph Ordering Attention Networks

Graph Neural Networks (GNNs) have been successfully used in many problems involving graph-structured data, achieving state-of-the-art performance. GNNs typically employ a message-passing scheme, in which every node aggregates information from its neighbors using a permutation-invariant aggregation function. Standard well-examined choices such as the mean or sum aggregation functions have limited capabilities, as they are not able to capture interactions among neighbors. In this work, we formalize these interactions using an information-theoretic framework that notably includes synergistic information. Driven by this definition, we introduce the Graph Ordering Attention (GOAT) layer, a novel GNN component that captures interactions between nodes in a neighborhood. This is achieved by learning local node orderings via an attention mechanism and processing the ordered representations using a recurrent neural network aggregator. This design allows us to make use of a permutation-sensitive aggregator while maintaining the permutation-equivariance of the proposed GOAT layer. The GOAT model demonstrates its increased performance in modeling graph metrics that capture complex information, such as the betweenness centrality and the effective size of a node. In practical use-cases, its superior modeling capability is confirmed through its success in several real-world node classification benchmarks.
Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networks

Deep regression is an important problem with numerous applications. These range from computer vision tasks such as age estimation from photographs, to medical tasks such as ejection fraction estimation from echocardiograms for disease tracking. Semi-supervised approaches for deep regression are notably under-explored compared to classification and segmentation tasks, however. Unlike classification tasks, which rely on thresholding functions for generating class pseudo-labels, regression tasks use real number target predictions directly as pseudo-labels, making them more sensitive to prediction quality. In this work, we propose a novel approach to semi-supervised regression, namely Uncertainty-Consistent Variational Model Ensembling (UCVME), which improves training by generating high-quality pseudo-labels and uncertainty estimates for heteroscedastic regression. Given that aleatoric uncertainty is only dependent on input data by definition and should be equal for the same inputs, we present a novel uncertainty consistency loss for co-trained models. Our consistency loss significantly improves uncertainty estimates and allows higher quality pseudo-labels to be assigned greater importance under heteroscedastic regression. Furthermore, we introduce a novel variational model ensembling approach to reduce prediction noise and generate more robust pseudo-labels. We analytically show our method generates higher quality targets for unlabeled data and further improves training. Experiments show that our method outperforms state-of-the-art alternatives on different tasks and can be competitive with supervised methods that use full labels. Code is available at https://github.com/xmed-lab/UCVME.
Technology system for product validation and algorithm test of GF common products and an application example

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Cross-Domain Graph Anomaly Detection via Anomaly-Aware Contrastive Alignment

Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled anomalous and normal nodes. Although it presents a promising approach to address the notoriously high false positive issue in anomaly detection, little work has been done in this line of research. There are numerous domain adaptation methods in the literature, but it is difficult to adapt them for GAD due to the unknown distributions of the anomalies and the complex node relations embedded in graph data. To this end, we introduce a novel domain adaptation approach, namely Anomaly-aware Contrastive alignmenT (ACT), for GAD. ACT is designed to jointly optimise: (i) unsupervised contrastive learning of normal representations of nodes in the target graph, and (ii) anomaly-aware one-class alignment that aligns these contrastive node representations and the representations of labelled normal nodes in the source graph, while enforcing significant deviation of the representations of the normal nodes from the labelled anomalous nodes in the source graph. In doing so, ACT effectively transfers anomaly-informed knowledge from the source graph to learn the complex node relations of the normal class for GAD on the target graph without any specification of the anomaly distributions. Extensive experiments on eight CD-GAD settings demonstrate that our approach ACT achieves substantially improved detection performance over 10 state-of-the-art GAD methods. Code is available at https://github.com/QZ-WANG/ACT.
Learning Interpretable Temporal Properties from Positive Examples Only

We consider the problem of explaining the temporal behavior of black-box systems using human-interpretable models. Following recent research trends, we rely on the fundamental yet interpretable models of deterministic finite automata (DFAs) and linear temporal logic (LTL_f) formulas. In contrast to most existing works for learning DFAs and LTL_f formulas, we consider learning from only positive examples. Our motivation is that negative examples are generally difficult to observe, in particular, from black-box systems. To learn meaningful models from positive examples only, we design algorithms that rely on conciseness and language minimality of models as regularizers. Our learning algorithms are based on two approaches: a symbolic and a counterexample-guided one. The symbolic approach exploits an efficient encoding of language minimality as a constraint satisfaction problem, whereas the counterexample-guided one relies on generating suitable negative examples to guide the learning. Both approaches provide us with effective algorithms with minimality guarantees on the learned models. To assess the effectiveness of our algorithms, we evaluate them on a few practical case studies.