Reviewerly/exHarmony huggingface dataset:
Qubit readout enabled by qubit cloaking

Time-dependent drives play a crucial role in quantum computing efforts with circuit quantum electrodynamics. They enable single-qubit control and entangling logical operations, as well as qubit readout. However, their presence can lead to deleterious effects such as large ac-Stark shifts and unwanted qubit transitions ultimately reflected into reduced control or readout fidelities. Qubit cloaking was introduced by Lled\'o et al. [C. Lled\'o, R. Dassonneville, A. Moulinas et al., Nat. Commun. 14, 6313 (2023)] to temporarily decouple the qubit from the coherent photon population of a driven cavity, allowing for the application of arbitrary displacements to the cavity field while avoiding the deleterious effects on the qubit. For qubit readout, cloaking permits us to prearm the cavity with an, in principle, arbitrarily large number of photons, in anticipation of the qubit-state-dependent evolution of the cavity field, allowing for improved readout strategies. Here, we take a closer look at two such strategies: first, arm-and-release readout, introduced together with qubit cloaking, where after arming the cavity, the cloaking mechanism is released and the cavity field evolves under the application of a constant drive amplitude; and, second, an arm-and-longitudinal readout scheme, where the cavity drive amplitude is slowly modulated after the release. We show that the two schemes complement each other, offering an improvement over standard dispersive readout for any values of the dispersive interaction and the cavity decay rate, as well as any target measurement integration time. Our results provide a recommendation for improving qubit readout without changes to the standard circuit-QED architecture.
DBU-Net: Dual branch U-Net for tumor segmentation in breast ultrasound images

Breast ultrasound medical images often have low imaging quality along with unclear target boundaries. These issues make it challenging for physicians to accurately identify and outline tumors when diagnosing patients. Since precise segmentation is crucial for diagnosis, there is a strong need for an automated method to enhance the segmentation accuracy, which can serve as a technical aid in diagnosis. Recently, the U-Net and its variants have shown great success in medical image segmentation. In this study, drawing inspiration from the U-Net concept, we propose a new variant of the U-Net architecture, called DBU-Net, for tumor segmentation in breast ultrasound images. To enhance the feature extraction capabilities of the encoder, we introduce a novel approach involving the utilization of two distinct encoding paths. In the first path, the original image is employed, while in the second path, we use an image created using the Roberts edge filter, in which edges are highlighted. This dual branch encoding strategy helps to extract the semantic rich information through a mutually informative learning process. At each level of the encoder, both branches independently undergo two convolutional layers followed by a pooling layer. To facilitate cross learning between the branches, a weighted addition scheme is implemented. These weights are dynamically learned by considering the gradient with respect to the loss function. We evaluate the performance of our proposed DBU-Net model on two datasets, namely BUSI and UDIAT, and our experimental results demonstrate superior performance compared to state-of-the-art models.
Interpretability Methods for Graph Neural Networks

The emerging graph neural network models (GNNs) have demonstrated great potential and success for downstream graph machine learning tasks, such as graph and node classification, link prediction, entity resolution, and question answering. However, neural networks are “black-box” – it is difficult to understand which aspects of the input data and the model guide the decisions of the network. Recently, several interpretability methods for GNNs have been developed, aiming at improving the model’s transparency and fairness, thus making them trustworthy in decision-critical applications, leading to democratization of deep learning approaches and easing their adoptions. The tutorial is designed to offer an overview of the state-of-the-art interpretability techniques for graph neural networks, including their taxonomy, evaluation metrics, benchmarking study, and ground truth. In addition, the tutorial discusses open problems and important research directions.
DynamiSE: Dynamic Signed Network Embedding for Link Prediction

In real-world scenarios, dynamic signed networks are ubiquitous where edges have positive and negative sign semantics and evolve over time. Encoding the dynamics and sign semantics of the network simultaneously is challenging. Moreover, over-smoothing is inevitably introduced by the learning of network dynamics. Targeting this gap, we propose Dynamic Signed Network Embedding (DynamiSE), which effectively integrates the balance theory and ordinary differential equation (ODE) into node representation learning to construct a deeper dynamic signed graph neural network and capture the complex sign semantics formed by the two types of edges.
Effects of Attack Interference on Radio Signal Classification

Abstract Signal recognition and classification are urgent issues in the field of radio monitoring. Although there have been many academic publications on the automatic modulation classification of radio signals, these studies have focused on how to use the new algorithms to increase signal recognition accuracy, and there is a lack of consideration of the effect of interference on classification accuracy. Based on publicly available datasets, RF datasets for Machine Learning, this paper uses the deep learning algorithm to generate an attack interference dataset and studies the impact of attack interference and noise on the classification accuracy of radio signals. The experimental results are as follows. Firstly, the presence of attack interference generally reduces the accuracy of radio signal classification. Secondly, the power of attack interference has a direct relationship with classification accuracy. Thirdly, under the condition of attack interference, the classification performance trends of the three open-source public datasets are consistent. This further illustrates how optimizing the modulation classification performance in the presence of interference is the key to applying the algorithm to practice.
Multiclass Classification of Malicious URL Detection Using Machine Learning and Deep Learning

Because of the rapid growth in internet usage, cyber risks have increased, especially in the form of malicious URLs. These malicious URLs are used as a means to deceive and exploit unsuspecting users, resulting in severe consequences such as data breaches, financial loss, and system compromise. The research paper presents an analysis of various techniques for malicious URL detection. The paper provides an overview on the impacts of malicious URLs on cyber attacks, phishing attempts, and malware infections, Introduces the various types of malicious URLs, and also discusses their characteristics and common strategies used by attackers to distract the malicious intent. The paper aims to detect the various malicious URLs for which URLs will be safe, risky, or too risky. Experimental results show that the random forest classifier and Feed feed-forward neural Network algorithms give better performance which is 91.44% and 95.98% respectively. The experimental results also show that the suggested URL features and behaviors can significantly improve the ability to recognize malicious URLs. This implies that the suggested methodology might be regarded as an effective method of identifying malicious URLs.
Event Management Evolution Through Non-Fungible Tokens

This paper explores the transformative potential of Non-Fungible Tokens (NFTs) in sports event management. NFTs, based on blockchain technology, offer unique digital assets with inherent immutability, distinguishing them from traditional cryptocurrencies. The surge in NFT popularity during late 2021 and early 2022 opened new possibilities for blockchain adoption beyond finance. However, utilizing NFTs as a managerial tool faced challenges, including user experience and the lack of standardization. The bear market presented an opportunity for blockchain innovations, such as layer-two solutions, token standards like ERC-1155, and decentralized identity solutions. The objective of this study is to demonstrate how NFTs can revolutionize sports event management, combat ticket speculation, and enhance fan engagement. The research methodology involves a comparative analysis, combining qualitative and quantitative approaches. Firstly, an examination of current sports ticketing practices and potential NFT use cases is performed, followed by an evaluation of existing blockchain solutions and emerging technologies. The paper proposes NFTs as a new frontier in sports event management due to blockchain's properties: immutability, transparency, programmability, and interoperability. Use cases of NFTs in sports ticketing include Proof of Attendance Protocol (POAPs) for fan engagement, NFT-tickets with smart contract capabilities to combat scalping, subscription models with royalties for fans, and solutions for unsold tickets that reward loyal fans. The technological framework for NFT adoption in event management includes the ERC-1155 token standard for efficient ticketing, layer-two solutions for scalability, and decentralized identity solutions for secure verification. Considering environmental impact, blockchain solutions with Proof of Stake consensus algorithms are favored. In conclusion, NFTs offer a disruptive approach to sports event management, combating ticket speculation, and enhancing fan engagement. Gradual adoption and public education are crucial, and the choice of blockchain and platform will depend on specific needs. The potential benefits are promising for the sports industry's future. By employing a comparative research methodology, combining qualitative and quantitative analyses, this paper provides comprehensive insights into the transformative impact of NFTs on sports event management.
Development Framework for Web-Based VR Tours and Its Examples

This paper treats a development framework for web-based VR tours of 360VR camera images/videos and lidar scanner data and introduce several example VR tours actually developed using the framework. Because of the advancement of XR(VR/AR/MR) technologies such as lidar scanners, 360VR cameras and high specification VR goggles, the demand of XR applications has become increased. However, the development of such XR applications is tedious work that needs much time. Therefore, the authors have proposed the development framework for web-based VR tours of 360VR camera images/videos and lidar scanner data and this paper introduces it and several VR tours as its examples.
Analyzing Spatio-Temporal Correlations with User-Oriented Guidance - An Interactive Visualization Approach for Demand-Oriented Limited Service Offers

GeoVisual Analytics often uses a wide range of visualization techniques in combination with human interactions. However, in order to improve urban planning decisions and expand the knowledge base about spatio-temporal correlations in cities, we identify high and sometimes difficult-to-overcome data and visualization complexity. Therefore, users need targeted support to reduce this complexity and understand complex multidimensional data. This paper proposes an interactive 3D visualization approach for analyzing spatio-temporal correlations. Effective visual explanations in user-oriented guidance with storytelling elements support this technique. We adapt the approach of flow maps to visualize both resulting correlations and the underlying density of possible destinations from an existing geoanalytical process. Our interactive step-by-step guidance with four narratives aims to create a balance between the author and the user. We evaluated our prototype with various domain experts. The evaluation confirms our approach and improves users' understanding of data and spatio-temporal correlations in urban areas.
Building Ensemble of Deep Networks: Convolutional Networks and Transformers

This paper presents a study on an automated system for image classification, which is based on the fusion of various deep learning methods. The study explores how to create an ensemble of different Convolutional Neural Network (CNN) models and transformer topologies that are fine-tuned on several datasets to leverage their diversity. The research question addressed in this work is whether different optimization algorithms can help in developing robust and efficient machine learning systems to be used in different domains for classification purposes. To do that, we introduce new Adam variant approaches. We employed these new approaches, coupled with several CNN topologies, for building an ensemble of classifiers that outperform both other Adam-based methods and stochastic gradient descent. Additionally, the study combines the ensemble of CNNs with an ensemble of transformers based on different topologies, such as Deit, Vit, Swin, and Coat. To the best of our knowledge, this is the first work in which an indepth study of a set of transformers and convolutional neural networks in a large set of small/medium-sized images is carried out. The experiments conducted on several datasets demonstrate that the combination of such different models results in a substantial performance improvement in all tested problems. All resources are available at https://github.com/LorisNanni.
mr2vec: Multiple role-based social network embedding

Learning representation of networks is increasingly important in social network analysis. Network embedding techniques have a wide range of applications in various downstream tasks, such as node classification and clustering. However, many existing approaches focus on representing each node in a network as a single vector, which is often not practical. In this paper, we propose a multiple role-based social network embedding, called mr2vec, by identifying multiple roles of each node through overlapping clustering and encoding each of them to a vector. Our proposed algorithm transforms a given network into the persona graph using overlapping clustering. Each pair of nodes and roles is mapped to a persona node, enabling us to learn multi-role-based embedding using conventional network embedding techniques. Moreover, we develop a new loss function to have close vector representations when the similarity between the nodes in a persona graph is high based not only on the structural information of node, but also on the role information of nodes. We conducted link prediction experiments within the network to demonstrate the effectiveness of representation learning that takes into account the multiple role information discovered through overlapping clustering of each node. Through extensive experiments on social networks, we have shown that mr2vec achieves higher accuracy than the state-of-the-art algorithms for single- or multi-aspect network embedding.
Multi-resolution distillation for self-supervised monocular depth estimation

Obtaining dense depth ground-truth is not trivial, which leads to the introduction of self-supervised monocular depth estimation. Most self-supervised methods utilize the photometric loss as the primary supervisory signal to optimize a depth network. However, such self-supervised training often falls into an undesirable local minimum due to the ambiguity of the photometric loss. In this paper, we propose a novel self-distillation training scheme that provides a new self-supervision signal, depth consistency among different input resolutions, to the depth network. We further introduce a gradient masking strategy that adjusts the self-supervision signal of the depth consistency during back-propagation to boost the effectiveness of our depth consistency. Experiments demonstrate that our method brings meaningful performance improvements when applied to various depth network architectures. Furthermore, our method outperforms the existing self-supervised methods on KITTI, Cityscapes, and DrivingStereo datasets by a noteworthy margin.
Behavioral analysis of bar charts in documents via stochastic petri-net modeling

The accurate understanding of documents depends on the effective processing of its individual modalities such as text, diagrams, tables, charts, and etc. While many research papers focus on extracting the illustrated values in bars charts, little work has been conduced regarding the analysis of this data to deduce behavioral information. In this paper, we present a methodology for the recognition and behavioral analysis of bar chart images. In particular, a Convolutional Neural Network model is trained for the initial chart classification and keypoints are extracted for the translation of identified columns into curves. By analyzing the curves associations and interactions with each other, and converting them into Stochastic Petri-nets, the methodology can perform behavioral analysis and deduce their functional characteristics. Empirical evaluation against state-of-the-art chart analysis tools shows high user-approval scores for the proposed method regarding the depth of extracted information and quality of responses.
Research on Quantitative Assessment and Dynamic Reasoning Method for Emergency Response Capability in Prefabricated Construction Safety

In response to the common issues of lacking a comprehensive quantitative assessment system and insufficient dynamic understanding of emergency response capability in prefabricated construction safety, this study proposes a research methodology based on decision-making trial and evaluation laboratory (DEMATEL) and fuzzy cognitive maps (FCM) to promote the construction of emergency response capacity. Firstly, a quantitative evaluation indicator system comprising 4 core categories of organizational management, personnel quality, technical measures, and emergency resources, along with 16 main categories, is established using grounded theory and three levels of coding approach. Subsequently, through a combination of expert surveys and quantitative analysis, DEMATEL is employed to unveil the causal relationships and key indicators of the evaluation criteria. Next, the DEMATEL and FCM models are integrated to conduct predictive and diagnostic reasoning analysis based on key indicators. Finally, a case study is conducted to validate the usability and effectiveness of the proposed model and methodology. The results demonstrate that indicators related to organizational management and personnel quality belong to the cause group, while technical measures and emergency resources fall into the effect group. The “completeness of emergency plans” exhibits the most significant influence on other indicators and is also the most influenced indicator by others. Predictive reasoning analysis reveals that well-controlled “emergency organizational structure and procedures” are crucial for enhancing emergency response capacity. Diagnostic reasoning analysis indicates that the improvement of emergency response capability should focus on enhancing the “completeness of emergency plans”. The synergistic effect between “emergency organizational structure and procedures” and “completeness of emergency plans” contributes to the enhancement of emergency response capability in prefabricated construction safety. The study holds both theoretical and practical significance for advancing safety management in prefabricated construction. Considering the dynamic coupling of multiple factors will be the primary direction of research in the field of safety management in the future.
Modeling the Photovoltaic Power Generation in Poland in the Light of PEP2040: An Application of Multiple Regression

This paper presents the results of research on the development of photovoltaic systems in Poland. The authors’ goal was to identify factors that can potentially shape the dynamics of solar energy development in Poland and that will affect the implementation of the PEP2040 goals. The authors also wanted to find a forecasting method that would enable the introduction of many explanatory variables—a set of identified factors—into the model. After an initial review of the literature, the ARMAX and MLR models were considered. Finally, taking into account MAPE errors, multiple regression was used for the analysis, the error of which was 0.87% (minimum 3% for the ARMAX model). The model was verified based on Doornik–Hansen, Breusch–Pagan, Dickey–Fuller tests, information criteria, and ex post errors. The model indicated that LCOE, CO2 emissions, Cu consumption, primary energy consumption, patents, GDP, and installed capacity should be considered statistically significant. The model also allowed us to determine the nature of the variables. Additionally, the authors wrote the WEKR 2.0 program, which allowed to determine the necessary amount of critical raw materials needed to build the planned PV energy generating capacity. Solar energy in Poland currently covers about 5% of the country’s electricity demand. The pace of development of photovoltaic installations has exceeded current expectations and forecasts included in the Polish Energy Policy until 2040 (PEP2040). The built model showed that if the explanatory variables introduced into the model continue to be subject to the same trends shaping them, a dynamic increase in photovoltaic energy production should be expected by 2025. The model indicates that the PEP2040 goal of increasing the installed capacity to 16 GW by 2040 can be achieved already in 2025, where the PV production volume could reach 8921 GWh. Models were also made taking into account individual critical raw materials such as Cu, Si, Ge, and Ga. Each of them showed statistical significance, which means that access to critical raw materials in the future will have a significant impact on the further development of photovoltaic installations.
Enhancing Elderly Fall Detection through IoT-Enabled Smart Flooring and AI for Independent Living Sustainability

In the realm of sustainable IoT and AI applications for the well-being of elderly individuals living alone in their homes, falls can have severe consequences. These consequences include post-fall complications and extended periods of immobility on the floor. Researchers have been exploring various techniques for fall detection over the past decade, and this study introduces an innovative Elder Fall Detection system that harnesses IoT and AI technologies. In our IoT configuration, we integrate RFID tags into smart carpets along with RFID readers to identify falls among the elderly population. To simulate fall events, we conducted experiments with 13 participants. In these experiments, RFID tags embedded in the smart carpets transmit signals to RFID readers, effectively distinguishing signals from fall events and regular movements. When a fall is detected, the system activates a green signal, triggers an alarm, and sends notifications to alert caregivers or family members. To enhance the precision of fall detection, we employed various machine and deep learning classifiers, including Random Forest (RF), XGBoost, Gated Recurrent Units (GRUs), Logistic Regression (LGR), and K-Nearest Neighbors (KNN), to analyze the collected dataset. Results show that the Random Forest algorithm achieves a 43% accuracy rate, GRUs exhibit a 44% accuracy rate, and XGBoost achieves a 33% accuracy rate. Remarkably, KNN outperforms the others with an exceptional accuracy rate of 99%. This research aims to propose an efficient fall detection framework that significantly contributes to enhancing the safety and overall well-being of independently living elderly individuals. It aligns with the principles of sustainability in IoT and AI applications.