Reviewerly/exHarmony huggingface dataset:
Reversing Unknown Qubit-Unitary Operation, Deterministically and Exactly

We report a deterministic and exact protocol to reverse any unknown qubit-unitary operation, which simulates the time inversion of a closed qubit system. To avoid known no-go results on universal deterministic exact unitary inversion, we consider the most general class of protocols transforming unknown unitary operations within the quantum circuit model, where the input unitary operation is called multiple times in sequence and fixed quantum circuits are inserted between the calls. In the proposed protocol, the input qubit-unitary operation is called 4 times to achieve the inverse operation, and the output state in an auxiliary system can be reused as a catalyst state in another run of the unitary inversion. We also present the simplification of the semidefinite programming for searching the optimal deterministic unitary inversion protocol for an arbitrary dimension presented by M. T. Quintino and D. Ebler [Quantum 6, 679 (2022)]. We show a method to reduce the large search space representing all possible protocols, which provides a useful tool for analyzing higher-order quantum transformations for unitary operations.
Sentiment analysis in multilingual context: Comparative analysis of machine learning and hybrid deep learning models

This research paper investigates the efficacy of various machine learning models, including deep learning and hybrid models, for text classification in the English and Bangla languages. The study focuses on sentiment analysis of comments from a popular Bengali e-commerce site, "DARAZ," which comprises both Bangla and translated English reviews. The primary objective of this study is to conduct a comparative analysis of various models, evaluating their efficacy in the domain of sentiment analysis. The research methodology includes implementing seven machine learning models and deep learning models, such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional 1D (Conv1D), and a combined Conv1D-LSTM. Preprocessing techniques are applied to a modified text set to enhance model accuracy. The major conclusion of the study is that Support Vector Machine (SVM) models exhibit superior performance compared to other models, achieving an accuracy of 82.56% for English text sentiment analysis and 86.43% for Bangla text sentiment analysis using the porter stemming algorithm. Additionally, the Bi-LSTM Based Model demonstrates the best performance among the deep learning models, achieving an accuracy of 78.10% for English text and 83.72% for Bangla text using porter stemming. This study signifies significant progress in natural language processing research, particularly for Bangla, by enhancing improved text classification models and methodologies. The results of this research make a significant contribution to the field of sentiment analysis and offer valuable insights for future research and practical applications.
Protected Data Sharing using Attribute Based Encryption for Remote Data Checking in Cloud Environment

Businesses and people together use cloud storage to transfer their vast amounts of data to unknown servers at a significantly lower cost than traditional methods. Several methods have been made to investigate the many aspects of this application scenario, such as efficiency, dependability, and security. Service providers can search and retrieve encrypted data in a cloud storage scenario where customers share their outsourced information with other users and convert their data before outputting it to a cloud storage service. The advanced technologies in cloud computing help them store and retrieve data securely anywhere at any time. Subsequently, in the cloud environment, security problem becomes a massive issue that defends further development in cloud computing and promotes remote data auditing. Several data auditing methods have been discussed in the research fiction, but those schemas still need to be improved for protected data sharing in cloud environments. This paper proposes a data-sharing schema for remote data auditing in a cloud environment. Our proposed method implements the CP-ABE (Attribute-based encryption) schema with minor changes in the access control policy structure to make it suitable for remote data auditing in the cloud environment. The experimental result shows that our protected data-sharing method can reduce data overhead when the user remotely shares the data in the cloud environment.
Spatial and Semantic Consistency Contrastive Learning for Self-Supervised Semantic Segmentation of Remote Sensing Images

A critical requirement for the success of supervised deep learning lies in having numerous annotated images, which is often challenging to fulfill in remote sensing semantic segmentation tasks. Self-supervised contrastive learning (CL) offers a strategy for learning general feature representations by pre-training neural networks on vast amounts of unlabeled data and subsequently fine-tuning them on downstream tasks with limited annotations. However, the vast majority of CL methods are designed based on instance discriminative pretext tasks, focusing solely on learning the global representation of the entire image while disregarding the essential spatial and semantic correlations crucial for semantic segmentation tasks. To address the above issues, in this paper, we propose a spatial and semantic consistency contrastive learning (SSCCL) framework for the semantic segmentation task of remote sensing images. Specifically, a consistency branch in SSCCL is designed to learn feature representations with spatial and semantic consistency by maximizing the similarity of the overlapping regions of the two augmented views. Additionally, an instance branch is introduced to learn global representations by enforcing the similarity of two augmented views from one image. Through the integration of the consistency branch and instance branch, the proposed SSCCL framework can learn robust and informative feature representations for semantic segmentation in remote sensing scenarios. The proposed method was evaluated on three publicly available remote sensing semantic segmentation datasets, and the experimental results show that our method achieves superior segmentation performance with limited annotations compared to state-of-the-art CL methods as well as ImageNet pre-training method.
Multi-Task Learning for UAV Aerial Object Detection in Foggy Weather Condition

Adverse weather conditions such as haze and snowfall can degrade the quality of captured images and affect performance of drone detection. Therefore, it is challenging to locate and identify targets in adverse weather scenarios. In this paper, a novel model called Object Detection in a Foggy Condition with YOLO (ODFC-YOLO) is proposed, which performs image dehazing and object detection jointly by multi-task learning approach. Our model consists of a detection subnet and a dehazing subnet, which can be trained end-to-end to optimize both tasks. Specifically, we propose a Cross-Stage Partial Fusion Decoder (CSP-Decoder) in the dehazing subnet to recover clean features of encoder from complex weather conditions, thereby reducing the feature discrepancy between hazy and clean images, thus enhancing the feature consistency between different tasks. Additionally, to increase the feature modeling and representation capabilities of our network, we also propose an efficient Global Context Enhanced Extraction (GCEE) module to extract beneficial information from blurred images by constructing global feature context long-range dependencies. Furthermore, we propose a Correlation-Aware Aggregated Loss (CAALoss) to average noise patterns and tune gradient magnitudes across different tasks, accordingly implicitly enhancing data diversity and alleviating representation bias. Finally, we verify the advantages of our proposed model on both synthetic and real-world foggy datasets, and our ODFC-YOLO achieves the highest mAP on all datasets while achieving 36 FPS real-time detection speed.
STUDY TO BUILD A GOODS ARRANGING AND CLASSIFICATION SYSTEM BASED ON INTERNET OF THINGS

In this paper, we focus on researching and building a system with the function of automatically classifying or classifying products assigned QR Code codes, applying Internet of Things technology and image processing techniques. The system collects information about the QR Code assigned on the input product from the Camera and then based on the rules that have been previously designed and programmed to stack or put the products in the desired position. Through the LabVIEW platform, the system analyzes any product image with high speed and accuracy (reaching 99%). Actual quantitative results are obtained based on conducting five specific experimental cases. The product's functions meet the requirements of the actual sorting or classification of goods, can be mass-installed at low cost, and is easy to use and maintain.
Incremental permutation feature importance (iPFI): towards online explanations on data streams

Abstract Explainable artificial intelligence has mainly focused on static learning scenarios so far. We are interested in dynamic scenarios where data is sampled progressively, and learning is done in an incremental rather than a batch mode. We seek efficient incremental algorithms for computing feature importance (FI). Permutation feature importance (PFI) is a well-established model-agnostic measure to obtain global FI based on feature marginalization of absent features. We propose an efficient, model-agnostic algorithm called iPFI to estimate this measure incrementally and under dynamic modeling conditions including concept drift. We prove theoretical guarantees on the approximation quality in terms of expectation and variance. To validate our theoretical findings and the efficacy of our approaches in incremental scenarios dealing with streaming data rather than traditional batch settings, we conduct multiple experimental studies on benchmark data with and without concept drift.
Quantum approximate optimization algorithm parameter prediction using a convolutional neural network

Abstract The Quantum approximate optimization algorithm (QAOA) is a quantum-classical hybrid algorithm aiming to produce approximate solutions for combinatorial optimization problems. In the QAOA, the quantum part prepares a quantum parameterized state that encodes the solution, where the parameters are optimized by a classical optimizer. However, it is difficult to find optimal parameters when the quantum circuit becomes deeper. Hence, there is numerous active research on the performance and the optimization cost of QAOA. In this work, we build a convolutional neural network to predict parameters of depth p + 1 QAOA instance by the parameters from the depth p QAOA counterpart. We propose two strategies based on this model. First, we recurrently apply the model to generate a set of initial values for a certain depth QAOA. It successfully initiates depth 10 QAOA instances, whereas each model is only trained with the parameters from depths less than 6. Second, the model is applied repetitively until the maximum expected value is reached. An average approximation ratio of 0.9759 for Max-Cut over 264 Erdős–Rényi graphs is obtained, while the optimizer is only adopted for generating the first input of the model.
Analysis and Evaluation of Feature Selection and Feature Extraction Methods

Abstract Hand gestures are widely used in human-to-human and human-to-machine communication. Therefore, hand gesture recognition is a topic of great interest. Hand gesture recognition is closely related to pattern recognition, where overfitting can occur when there are many predictors relative to the size of the training set. Therefore, it is necessary to reduce the dimensionality of the feature vectors through feature selection techniques. In addition, the need for portability in hand gesture recognition systems limits the use of deep learning algorithms. In this sense, a study of feature selection and extraction methods is proposed for the use of traditional machine learning algorithms. The feature selection methods analyzed are: maximum relevance and minimum redundancy (MRMR), Sequential, neighbor component analysis without parameters (NCAsp), neighbor component analysis with parameters (NCAp), Relief-F, and decision tree (DT). We also analyze the behavior of feature selection methods using classification and recognition accuracy and processing time. Feature selection methods were fed through seventeen feature extraction functions, which return a score proportional to its importance. The functions are then ranked according to their scores and fed to machine learning algorithms such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (DT). This work demonstrates that all feature selection methods evaluated on ANN provide better accuracy. In addition, the combination and number of feature extraction functions influence the accuracy and processing time.
Exploring new depths: Applying machine learning for the analysis of student argumentation in chemistry

Abstract Constructing arguments is essential in science subjects like chemistry. For example, students in organic chemistry should learn to argue about the plausibility of competing chemical reactions by including various sources of evidence and justifying the derived information with reasoning. While doing so, students face significant challenges in coherently structuring their arguments and integrating chemical concepts. For this reason, a reliable assessment of students' argumentation is critical. However, as arguments are usually presented in open‐ended tasks, scoring assessments manually is resource‐consuming and conceptually difficult. To augment human diagnostic capabilities, artificial intelligence techniques such as machine learning or natural language processing offer novel possibilities for an in‐depth analysis of students' argumentation. In this study, we extensively evaluated students' written arguments about the plausibility of competing chemical reactions based on a methodological approach called computational grounded theory . By using an unsupervised clustering technique, we sought to evaluate students' argumentation patterns in detail, providing new insights into the modes of reasoning and levels of granularity applied in students' written accounts. Based on this analysis, we developed a holistic 20‐category rubric by combining the data‐driven clusters with a theory‐driven framework to automate the analysis of the identified argumentation patterns. Pre‐trained large language models in conjunction with deep neural networks provided almost perfect machine‐human score agreement and well‐interpretable results, which underpins the potential of the applied state‐of‐the‐art deep learning techniques in analyzing students' argument complexity. The findings demonstrate an approach to combining human and computer‐based analysis in uncovering written argumentation.
Postdigital Feminisms

Postdigital feminisms represent an eclectic field of critical scholarship, activism, websites, protests, artistic and popular works, which critique the systemic gender inequalities of the postdigital condition. Just as women are not a homogenous entity, postdigital feminisms respond to varying guises of patriarchy while grounded in contextual issues. This addresses gaps in postdigital theory concerning deep-seated gender biases of technologies, education, and platform patriarchy. Simultaneously, postdigital feminisms develop creative, resistive, and radical challenges to hegemonic visibilities of certain women while obscuring the voices and perspectives of others.
xAI-CycleGAN, a Cycle-Consistent Generative Assistive Network

In the domain of unsupervised image-to-image transformation using generative transformative models, CycleGAN [7] has become the architecture of choice. One of the primary downsides of this architecture is its relatively slow rate of convergence. In this work, we use discriminator-driven explainability to speed up the convergence rate of the generative model by using saliency maps from the discriminator that mask the gradients of the generator during backpropagation, based on the work of Nagisetty et al. [3], and also introducing the saliency map on input, added onto a Gaussian noise mask, by using an interpretable latent variable based on Wang M.'s Mask CycleGAN [5]. This allows for an explainability fusion in both directions, and utilizing the noise-added saliency map on input as evidence-based counterfactual filtering [4]. This new architecture has much higher rate of convergence than a baseline CycleGAN architecture while preserving the image quality.
Cybersecurity Risk Analysis in the IoT: A Systematic Review

The Internet of Things (IoT) is increasingly becoming a part of our daily lives, raising significant concerns about future cybersecurity risks and the need for reliable solutions. This study conducts a comprehensive systematic literature review to examine the various challenges and attacks threatening IoT cybersecurity, as well as the proposed frameworks and solutions. Furthermore, it explores emerging trends and identifies existing gaps in this domain. The study’s novelty lies in its extensive exploration of machine learning techniques for detecting and countering IoT threats. It also contributes by highlighting research gaps in economic impact assessment and industrial IoT security. The systematic review analyzes 40 articles, providing valuable insights and guiding future research directions. Results show that privacy issues and cybercrimes are the primary concerns in IoT security, and artificial intelligence holds promise for future cybersecurity. However, some attacks remain inadequately addressed by existing solutions, such as confidentiality, security authentication, and data server connection attacks, necessitating further research and real-life testing of proposed remedies.
Blockchain-Enabled Microgrids: Toward Peer-to-Peer Energy Trading and Flexible Demand Management

The energy transition to a decarbonized energy scenario leads toward distributed energy resources in which end users can both generate and consume renewable electricity. As a result, several challenges arise in terms of decentralized energy resource management and grid reliability. With microgrids, the cooperation of distributed energy resources is improved, and with peer-to-peer energy exchange and demand response programs, better energy allocation and flexible management of consumption loads according to the needs of supply systems are achieved. However, effective peer-to-peer energy allocation and flexible demand management in microgrids require the development of market structures, pricing mechanisms, and demand response strategies enabled by a reliable communication system. In this field, blockchain offers a decentralized communication tool for energy transactions that can provide transparency, security, and immutability. Therefore, this paper provides a comprehensive review of key factors for peer-to-peer energy trading and flexible energy demand management in blockchain-enabled microgrids. The goal is to provide guidelines on the basic components that are useful in ensuring efficient operation of microgrids. Finally, using a holistic view of technology adoption as a tool for peer-to-peer communication in microgrids, this paper reviews projects aimed at implementing blockchain in energy trading and flexible demand management.
OWAdapt: An adaptive loss function for deep learning using OWA operators

In this paper, we propose a novel adaptive loss function for enhancing deep learning performance in classification tasks. Specifically, we redefine the cross-entropy loss to effectively address class-level noise conditions, including the challenging problem of class imbalance. Our approach introduces aggregation operators to improve classification accuracy. The rationale behind our proposed method lies in the iterative up-weighting of class-level components within the loss function, focusing on those with larger errors. To achieve this, we employ the ordered weighted average (OWA) operator and combine it with an adaptive scheme for gradient-based learning. The main finding is that our method outperforms other commonly used loss functions, such as the standard cross-entropy or focal loss, across various binary and multiclass classification tasks. Furthermore, we explore the influence of hyperparameters associated with the OWA operators and propose a default configuration that performs well across different experimental settings.
A cybersecurity risk quantification and classification framework for informed risk mitigation decisions

Data breaches have been causing havoc for many years and continue to rise, demanding a more comprehensive framework to assess their severity. This study proposes a cybersecurity risk quantification and classification framework, addressing a significant research gap in breach severity evaluation. The study has two main objectives: to present a structured model for precise breach severity assessment, and to apply this model to real-world data breaches for practical insights. To achieve these objectives, we utilize a content analysis methodology to gauge severity of data breaches. This is followed by a robust likelihood impact matrix analysis, serving as a decision analytics tool, to effectively quantify potential risks. The integration of these approaches yields a more comprehensive and nuanced evaluation for assessing data breach severity. The proposed framework is then applied to data breaches gathered from Standard & Poor’s (S&P 500) organizations, revealing hacking and loss/theft of portable media as the breach types with the highest impact and probability. Drawing from these insights, our study suggests effective strategies to mitigate data breach risks for organizations. The implications of our research findings offer valuable contributions to both academic research and practical cybersecurity risk management.
Design of intelligent hybrid NAR-GRNN paradigm for fractional order VDP chaotic system in cardiac pacemaker with relaxation oscillator

Managing cardiac disease and abnormal heart rate variability is a challenging problem with its psychological impact on lifesaving intervention. The research presents a novel machine learning approach to paradigm dynamic pacemaker design based on fractional order modified Van der Pol oscillator system implemented to generate rich non-sinusoidal signals for cardiac intervention and treatment. The physical framework of the novel contribution comprises Sino-Atrial (SA) and Atrio-Ventricular (AV) nodes, designed to utilize fractional order excitation signals derived from the VDP dynamic system. Hybrid paradigm is designed by integrating the dynamical nonlinear autoregressive (NAR) neural networks and generalized regression neural networks (GRNNs) to analyze the parametric fractal impulse model for cardiac muscle. The chaotic pattern of the proposed nonlinear system is explored in terms of phase portraits and Lyapunov exponents. The exceptional performance of the cutting-edge fractional order multimodal computing paradigm is validated by achieving an RMSE of 0.1E-14. The dynamic chaotic and fractal trajectory of relaxation oscillator based on its computed control parameters can provide valuable insight to design robust and efficient electronic implantable cardioverter defibrillators (ICDs) as pacemakers to stabilize cardiac impulse variability and alleviate healthcare burden as compared to traditional cardiac rehabilitation procedures.