Reviewerly/exHarmony huggingface dataset:
Flow-preserving ZX-calculus Rewrite Rules for Optimisation and Obfuscation

In the one-way model of measurement-based quantum computation (MBQC), computation proceeds via measurements on a resource state.So-called flow conditions ensure that the overall computation is deterministic in a suitable sense, with Pauli flow being the most general of these.Computations, represented as measurement patterns, may be rewritten to optimise resource use and for other purposes.Such rewrites need to preserve the existence of flow to ensure the new pattern can still be implemented deterministically.The majority of existing work in this area has focused on rewrites that reduce the number of qubits, yet it can be beneficial to increase the number of qubits for certain kinds of optimisation, as well as for obfuscation.In this work, we introduce several ZX-calculus rewrite rules that increase the number of qubits and preserve the existence of Pauli flow.These rules can be used to transform any measurement pattern into a pattern containing only (general or Pauli) measurements within the XY-plane.We also give the first flow-preserving rewrite rule that allows measurement angles to be changed arbitrarily, and use this to prove that the 'neighbour unfusion' rule of Staudacher et al. preserves the existence of Pauli flow.This implies it may be possible to reduce the runtime of their two-qubit-gate optimisation procedure by removing the need to regularly run the costly gflow-finding algorithm.
3D Visualization in Digital Medicine Using XR Technology

Nowadays, virtual reality is a new and rapidly developing technology that provides the opportunity for a new, more immersive form of data visualization. Evaluating digitized pathological serial sections and establishing the appropriate diagnosis is one of the key task of the pathologist in the daily work. The type of tools used by pathologists in the evaluation of samples has not changed much in recent decades. On the other hand, the amount of information required to establish an accurate diagnosis has been significantly increased. Nowadays, pathologists are working with the help of multiple high-resolution desktop monitors. Instead of the large screens, the use of virtual reality can serve as an alternative solution, which provides virtualized working space for pathologists during routine sample evaluation. In our research, we defined a new immersive working environment for pathologists. In our proposed solution we visualize several type of digitized medical image data with the corresponding meta data in 3D, and we also defined virtualized functions that support the evaluation process. The main aim of this paper is to present the new possibilities provided by 3D visualization and virtual reality in digital pathology. The paper presents a new virtual reality-based examination environment, as well as software functionalities that are essential for 3D pathological tissue evaluation.
Enhancing Recommendation System using Ontology-based Similarity and Incremental SVD Prediction

Background: With the explosion of data in recent years, recommender systems have become increasingly important for personalized services and enhancing user engagement in various industries, including e-commerce and entertainment. Collaborative filtering (CF) is a widely used approach for generating recommendations, but it has limitations in addressing issues such as sparsity, scalability, and prediction errors. Methods: To address these challenges, this study proposes a novel hybrid CF method for movie recommendations that combines an incremental singular value decomposition approach with an item-based ontological semantic filtering approach in both online and offline phases. The ontology-based technique improves the accuracy of predictions and recommendations. The proposed method is evaluated on a real-world movie recommendation dataset using several performance metrics, including precision, F1 scores, and MAE. Results: The results demonstrate that the proposed method outperforms existing methods in terms of accuracy while also addressing sparsity and scalability issues in recommender systems. Additionally, our approach has the advantage of reduced running time, making it a promising solution for practical applications. Conclusion: The proposed method offers a promising solution to the challenges faced by traditional CF methods in recommender systems. By combining incremental SVD and ontological semantic filtering, the proposed method not only improves the accuracy of predictions and recommendations but also addresses issues related to scalability and sparsity. Overall, the proposed method has the potential to contribute to the development of more accurate and efficient recommendation systems in various industries, including e-commerce and entertainment.
A language framework for modeling social media account behavior

Abstract Malicious actors exploit social media to inflate stock prices, sway elections, spread misinformation, and sow discord. To these ends, they employ tactics that include the use of inauthentic accounts and campaigns. Methods to detect these abuses currently rely on features specifically designed to target suspicious behaviors. However, the effectiveness of these methods decays as malicious behaviors evolve. To address this challenge, we propose a language framework for modeling social media account behaviors. Words in this framework, called BLOC, consist of symbols drawn from distinct alphabets representing user actions and content. Languages from the framework are highly flexible and can be applied to model a broad spectrum of legitimate and suspicious online behaviors without extensive fine-tuning. Using BLOC to represent the behaviors of Twitter accounts, we achieve performance comparable to or better than state-of-the-art methods in the detection of social bots and coordinated inauthentic behavior.
Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey

This paper explores the potential for communication-efficient federated learning (FL) in modern distributed systems. FL is an emerging distributed machine learning technique that allows for the distributed training of a single machine learning model across multiple geographically distributed clients. This paper surveys the various approaches to communication-efficient FL, including model updates, compression techniques, resource management for the edge and cloud, and client selection. We also review the various optimization techniques associated with communication-efficient FL, such as compression schemes and structured updates. Finally, we highlight the current research challenges and discuss the potential future directions for communication-efficient FL.
Sequential Modeling and Knowledge Source Integration for Identifying the Structure of a Bayesian Network for Multistage Process Monitoring and Diagnosis

Abstract As a popular applied artificial intelligence tool, Bayesian networks are increasingly being used to model multistage manufacturing processes for fault diagnosis purposes. However, the major issue limiting the practical adoption of Bayesian networks is the difficulty of learning the network structure for large multistage processes. Traditionally, Bayesian network structures are learned either with the help of domain experts or by utilizing data-driven structure learning algorithms through trial and error. Both approaches have their limitations. On the one hand, the expert-driven approach is costly, time-consuming, cumbersome for large networks, and susceptible to errors in assessing probabilities; on the other hand, data-driven approaches suffer from noise, biases, and inadequacy of training data and often fail to capture the physical causal structure of the data. Therefore, in this article, we propose a Bayesian network structure learning approach where popular manufacturing knowledge sources like the failure mode and effect analysis (FMEA) and hierarchical variable ordering are used as structural priors to guide the data-driven structure learning process. In addition, to introduce modularity and flexibility into the learning process, we present a sequential modeling approach for structure learning so that large multistage networks can be learned stage by stage progressively. Furthermore, through simulation studies, we compare and analyze the performance of the knowledge source–based structurally biased networks in the context of multistage process fault diagnosis.
Adaptive Guided Equilibrium Optimizer with Spiral Search Mechanism to Solve Global Optimization Problems

The equilibrium optimizer (EO) is a recently developed physics-based optimization technique for complex optimization problems. Although the algorithm shows excellent exploitation capability, it still has some drawbacks, such as the tendency to fall into local optima and poor population diversity. To address these shortcomings, an enhanced EO algorithm is proposed in this paper. First, a spiral search mechanism is introduced to guide the particles to more promising search regions. Then, a new inertia weight factor is employed to mitigate the oscillation phenomena of particles. To evaluate the effectiveness of the proposed algorithm, it has been tested on the CEC2017 test suite and the mobile robot path planning (MRPP) problem and compared with some advanced metaheuristic techniques. The experimental results demonstrate that our improved EO algorithm outperforms the comparison methods in solving both numerical optimization problems and practical problems. Overall, the developed EO variant has good robustness and stability and can be considered as a promising optimization tool.
Arbitrary style transfer based on Attention and Covariance-Matching

Arbitrary style transfer has broad application prospects and important research values, and it is a research hotspot in the field of computer vision. Many studies have shown that arbitrary style transfer achieves remarkable success. However, existing methods may produce artifacts and sometimes lead to distortion of the content structure. To overcome this limitation, this paper proposes a novel Attention-wise and Covariance-Matching Module (ACMM) that preserves the content structure well without unpleasant artifacts. First, our method uses global attention covariance matching to match the global information of style features with content features, thereby obtaining pleasing stylized images. Then, to enable the model to better match the global statistics, a histogram loss is introduced to improve the saturation and stability of the resulting color. Our method can preserve the content structure; so that appearance transfer can be achieved with simple adjustments to the model. The effectiveness of the proposed method is demonstrated through qualitative and quantitative experiments with comparisons with some state-of-the-art arbitrary style transfer methods.
A novel image compression method using wavelet coefficients and Huffman coding

Compressing medical images to reduce their size while maintaining their clinical and diagnostic information is crucial. Because medical images can be large and demand a lot of storage and transmission capacity, effective compression methods aid medical institutions in better storing and transmitting medical images, reducing costs, speeding up data transfer, and simplifying managing image databases. However, it is essential to note that image compression in medical imaging can also introduce drawbacks, such as loss of information and poor output image quality. Therefore, a suitable compression algorithm and parameter must be chosen to balance file size and visual fidelity. This paper suggests an effective image compression method employing the Discrete Wavelet Transform (DWT), followed by a reduction operation and Huffman coding to produce a mere lossless encoding to transmit the images over a channel. The extracted DWT coefficients are mapped to the nearest integral value. All four sub-bands of DWT are joined, and then a window of 3 ×3 is selected for reduction operation by choosing the origin as the pivot element. The Huffman coding algorithm is used to compress the processed image. The pivot origin element is used in the reversible reduction while uncompressing the image. When sending compressed data across an unreliable route, the window size and pivot element selection keep the compressed data secure. Standard measures such as bits per pixel (BPP) and compression ratio (CR) are used to assess the suggested approach. The efficiency of the suggested course of action is supported by the research's findings, which use a peak signal-to-noise ratio (PSNR) of 54.66 dB.
Integrated thrust ripple identification and compensation for linear servo system using an MP algorithm

Generally there are two methods for the thrust ripple modeling: one is to identify the eigenvalues and the model parameters of thrust ripple sequentially under rigorous operating conditions; another is to treat thrust ripple as a general and inaccurate disturbance. To get rid of the constraints and further enhance the tracking accuracy of the linear servo system, a novel integrated identification and compensation scheme using an improved matching pursuit algorithm is proposed in this paper. First, the dynamics of the linear motor is formulated, and the cause of thrust ripple is analyzed. Then, a time-frequency atomic library of thrust ripple is constructed with the consideration of the frequency characteristics of position command, and meanwhile, the two optimal atoms are obtained successively by maximizing the inner product for the thrust ripple reconstruction. Based on the above, the eigenvalues and model parameters of thrust ripple can be simultaneously identified in an online manner. Finally, the feedforward compensation component is devised by the identification results to suppress thrust ripple. In order to verify the superiority of the proposed scheme, simulations and experiments are conducted compared with the conventional methods.
A computationally efficient sequential convex programming using Chebyshev collocation method

This paper aims to develop a computationally efficient sequential convex programming algorithm for a class of nonlinear systems. A Chebyshev collocation discretization (CCD) technique is proposed that transforms the continuous convex optimization problem into a finite-dimensional discrete optimization problem. The CCD technique uses Chebyshev polynomials to construct a closed-form approximate solution to the convex dynamic equation. Moreover, it establishes a linear mapping between the control inputs and the system states. This enables the constraints of the dynamics equations to be externalized from the optimization problem and computed using efficient linear equation solving algorithms. On the one hand, this strategy significantly reduces the number of constraint equations and optimization variables, improving the speed of the optimizer. On the other hand, it preserves the path constraints on the system states in the optimization problem. A numerical simulation example of the perching maneuver for a fixed-wing unmanned aerial vehicle is presented to validate the efficiency of the algorithm. The result shows significant improvements in both speed and accuracy compared to the Euler discretization, and greatly improves the solution speed while maintaining almost the same solution accuracy compared to the Gauss pseudospectral optimization method.
Let's talk about Sex!: AI and relational factors in the adoption of a chatbot conveying sexual and reproductive health information

This study addresses the adoption intention of ANA Chatbot, developed as part of the "Chup! Jom Sembang Seks" (translated as "Wait a minute! Let's Talk about Sex") campaign in Malaysia. ANA Chatbot provides youths with comprehensive sexual and reproductive health (SRH) information, empowering them in this vital aspect of their lives. Despite the emerging interest in chatbots conveying SRH information, research on the factors affecting the technology's adoption remains scarce. This paper proposes an adoption model straddling AI attributes (perceived intelligence and anthropomorphism), relational elements (perceived risk and trust), and the UTAUT's factors (performance expectancy, effort expectancy, social influence, and facilitating conditions). We empirically analyzed the model using Partial Least Squares Structural Equation Modelling based on 243 valid responses from participants engaging with ANA Chatbot. The results indicate that perceived intelligence positively relates to anthropomorphism, functional elements (performance expectancy and effort expectancy), trust, and SRH chatbot adoption intention. Anthropomorphism enhances performance expectancy but does not affect effort expectancy, trust, and SRH chatbot adoption intention. Performance expectancy, effort expectancy, facilitating conditions, and social influence are antecedents to ANA Chatbot adoption intention, thus affirming the UTAUT model. Contrary to predictions, perceived risk and trust do not influence the intention to adopt ANA Chatbot. Overall, this study contributes theoretical knowledge concerning AI-centric technology adoption models with a unique and novel focus on an SRH chatbot while providing pragmatic recommendations for promoting SRH chatbot adoption intention.
A management knowledge graph approach for critical infrastructure protection: Ontology design, information extraction and relation prediction

Critical Infrastructures (CI) underpin the basic functioning of society and the economy. Proper governance of CI security management remains a crucial challenge. This study aims to construct a knowledge graph for modeling CI protection. While the previous research has focused on threat intelligence modeling and open knowledge bases, they miss considering the defense side. Accordingly, we propose a knowledge graph for critical infrastructure protection, CIPKG, that extends the management ontology to include the defense side. It addresses the cross-industry and cross-time information gaps that occur in the process of CI protection management, making it more comprehensive in structure than the existing knowledge graph. We employ simplified Structured Threat Information Expression as attack ontology and design a new ontology for the defense side, which could combine with the existing threat ontology to form the CI protection knowledge graph. To dynamically extract information from emerging knowledge, we employ a Bi-directional Long Short-Term Memory and Conditional Random Field model with pre-trained cybersecurity domain-specific Bidirectional Encoder Representations from Transformers to recognize the named entities from CI regulations and standards. To associate the threat part with the management portion of the knowledge graph, we adopt the Knowledge Graph Bidirectional Encoder Representations from Transformer model to capture the semantic information and predict the relationship between threat and management. After information extraction and relation prediction, we build a knowledge graph with 529,360 nodes and about 3,335,000 edges.
STEAMCODER: Spatial and Temporal Adaptive Dynamic Convolution Autoencoder for Anomaly Detection

The anomaly detection algorithm greatly improves the reliability of equipment operation. Traditional anomaly detection algorithms are mostly designed for large data sets, making it difficult to detect anomalies when where is not enough accumulated equipment data. Therefore, detecting anomalies during the cold-start stage is also challenging. In industrial production, the working conditions of the equipment are constantly adjusted, and the amount or characteristics of data for training also change frequently. Hence, an adaptive algorithm is required to address these problems. We design a novel algorithm called Spatial-TEmporal Adaptive dynaMic Convolution autoencODER for Anomaly Detection (STEAMCODER), specifically. This algorithm first converts the data into a spatial–temporal anomaly feature matrix and then utilizes a dynamic convolution autoencoder to analyze the matrix and detect anomalies. Finally, we conduct extensive experiments to validate the performance of STEAMCODER and the results demonstrated its superiority compared to state-of-the-art algorithms in adaptive anomaly detection, including F1 score and other indicators. Furthermore, STEAMCODER is capable of filtering false positives caused by glitch data, enabling the early detection of equipment anomalies.
Signed attention based graph neural network for graphs with heterophily

Graph Neural Networks (GNNs) are an effective deep learning methodology used to encode graph-structured data. Several popular GNNs have demonstrated excellent results in various downstream tasks, including node classification and visualization, especially when homophily occurs in graphs where connected nodes may share identical features or labels. Although recent models can extend GNNs beyond homophily, two fundamental weaknesses limit their expressive ability. First, most of the existing GNNs fail to adaptively aggregate information from different neighbors, especially negative information from different classes. Even worse, they fail to encode the positional information, leading to the inevitable conflation of information from different neighborhoods that should be specified with different importance. To overcome these limitations, we propose a novel graph neural network model based on the Signed Attention mechanism (SA), namely SAGNN. Specifically, the signed attention mechanism is presented to adaptively learn the complex relationship between exact neighboring nodes. Compared with the original non-negative attention mechanism, SA aggregates information from both positive and negative neighbors, especially on graphs with heterophily. Furthermore, we propose an Exactly Higher-Order Neighborhood Aggregation that implicitly encodes positional information to specify the importance of different neighborhoods. Extensive experiments conducted on eight benchmark datasets demonstrate that the proposed SAGNN model learns more robust and expressive representations, and achieves superior performance on node classification and visualization tasks.
Homogeneous–Heterogeneous Hybrid Ensemble for concept-drift adaptation

Homogeneous ensembles are very effective in concept-drift adaptation. However, choosing an appropriate base learner and its hyperparameters suitable for a stream is critical for their predictive performance. Moreover, the best base learner and its hyperparameters may change over time as the stream evolves, necessitating manual reconfiguration. On the other hand, heterogeneous ensembles train multiple base learners belonging to diverse algorithmic families with different inductive biases. Though it eliminates the need to manually choose the best base learner for a stream, their size is often restricted to the number of unique base learner algorithms, limiting their scalability. We combine the strengths of homogeneous and heterogeneous ensembles into a unified scalable ensemble framework with higher predictive performance, while eliminating the need to manually specify and adapt the optimal base learner and its hyperparameters for a stream. The proposed ensemble named H3E is a single-pass hybrid algorithm which uses a genetic algorithm (GA) based optimization in combination with stacking to provide high predictive performance at a competitive computational cost. Experiments on several real and synthetic data streams affected by diverse drift types confirm the superior predictive performance and utility of our approach in comparison to popular online ensembles.