Reviewerly/exHarmony huggingface dataset:
LiSA: A Generic Framework for Multilanguage Static Analysis

Modern software engineering revolves around distributed applications. From IoT networks to client-server infrastructures, the application code is increasingly being divided into separate sub-programs interacting with each other. As they are completely independent from each other, each such program is likely to be developed in a separate programming language, choosing the best fit for the task to at hand. From a static program analysis perspective, taking on a mixture of languages is challenging. This paper defines a generic framework where modular multilanguage static analyses can be defined through the abstract interpretation theory. The framework has been implemented in LiSA (Library for Static Analysis), an open-source Java library that provides the complete infrastructure necessary for developing static analyzers. LiSA strives to be modular, ensuring that all components taking part in the analysis are both easy to develop and highly interchangeable. LiSA also ensures that components are parametric to all language-specific features: semantics, execution model, and memory model are not directly encoded within the components themselves. A proof-of-concept instantiation is provided, demonstrating LiSA’s capability to analyze multiple languages in a single analysis through the discovery of an IoT vulnerability spanning C++ and Java code.
Transcending Pixels: Boosting Saliency Detection via Scene Understanding from Aerial Imagery

Existing remote sensing image salient object detection (RSI-SOD) methods widely perform object-level semantic understanding with pixel-level supervision, but ignore the image-level scene information. As a fundamental attribute of RSIs, the scene has a complex intrinsic correlation with salient objects, which may bring hints to improve saliency detection performance. However, existing RSI-SOD datasets lack both pixel- and image-level labels, and it is non-trivial to effectively transfer the scene domain knowledge for more accurate saliency localization. To address these challenges, we first annotate the image-level scene labels of three RSI-SOD datasets inspired by remote sensing scene classification. On top of it, we present a novel scene-guided dual-stream network (SDNet), which can perform cross-task knowledge distillation from the scene classification to facilitate accurate saliency detection. Specifically, a scene knowledge transfer module (SKTM) and a conditional dynamic guidance module (CDGM) are designed for extracting saliency key area as spatial attention from the scene subnet and guiding the saliency subnet to generate scene-enhanced saliency features, respectively. Finally, an object contour awareness module (OCAM) is introduced to enable the model to focus more on irregular spatial details of salient objects from the complicated background. Extensive experiments reveal that our SDNet outperforms over 20 state-of-the-art algorithms on three datasets. Moreover, we prove that the proposed framework is model-agnostic, and its extension to six baselines can bring significant performance benefits. Code will be available at https://github.com/lyf0801/SDNet.
Using WebXR Metaverse Platforms to Create Touristic Services and Cultural Promotion

In recent years, there has been a surge of Metaverse applications and tools striving to capture the attention of both the general public and businesses, with a particularly strong potential within the tourism sector. However, there has been significant criticism towards major corporations for marketing a concept of the Metaverse that fails to align with reality. On the other hand, smaller entities such as Spatial-io, which is an innovative metaverse platform, are introducing a different style of the Metaverse, one that is highly accessible from contemporary devices like smartphones, tablets, VR headsets, and traditional PCs via WebXR platforms. This article delves into and scrutinizes various methodologies of a tourism-oriented Metaverse, considering its prospective utility as a vehicle to attract more visitors. A virtual tourist information center was established on the Spatial-io Metaverse platform to promote Valencia, Spain. This research scrutinizes the navigation, accessibility, and usability of the service from a conventional PC browser, contrasting it with the experience offered by the Meta Quest 2 virtual reality headset. The study’s quantitative and qualitative data analysis indicates that these innovative services are highly regarded, particularly when a real person (not a bot) provides information, fostering trust and offering details about various tourist attractions within the promoted city. The comparison of user inquiries’ time and depth aligns with the immersion level, demonstrating more positive feedback when the service is accessed through the VR system rather than a standard PC browser.
A Tale of Two Markets: Investigating the Ransomware Payments Economy

A data-driven, follow-the-money approach to characterize the ransomware ecosystem uncovers two parallel ransomware criminal markets: commodity ransomware and Ransomware as a Service (RaaS).
Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record

The electronic health record (EHR) represents a rich source of patient information, increasingly being leveraged for cardiovascular research. Although its primary use remains the seamless delivery of health care, the various longitudinally aggregated structured and unstructured data elements for each patient within the EHR can define the computational phenotypes of disease and care signatures and their association with outcomes. Although structured data elements, such as demographic characteristics, laboratory measurements, problem lists, and medications, are easily extracted, unstructured data are underused. The latter include free text in clinical narratives, documentation of procedures, and reports of imaging and pathology. Rapid scaling up of data storage and rapid innovation in natural language processing and computer vision can power insights from unstructured data streams. However, despite an array of opportunities for research using the EHR, specific expertise is necessary to adequately address confidentiality, accuracy, completeness, and heterogeneity challenges in EHR-based research. These often require methodological innovation and best practices to design and conduct successful research studies. Our review discusses these challenges and their proposed solutions. In addition, we highlight the ongoing innovations in federated learning in the EHR through a greater focus on common data models and discuss ongoing work that defines such an approach to large-scale, multicenter, federated studies. Such parallel improvements in technology and research methods enable innovative care and optimization of patient outcomes.
Insu-YOLO: An Insulator Defect Detection Algorithm Based on Multiscale Feature Fusion

To keep the balance of precision and speed of unmanned aerial vehicles (UAVs) in detecting insulator defects during power inspection, an improved insulator defect identification algorithm, Insu-YOLO, which is based on the latest YOLOv8 network, is proposed in this paper. Firstly, to lower the computational complexity of the network, the GSConv module is introduced in the backbone and neck network. In the neck network, a lightweight content-aware reassembly of features (CARAFE) structure is adopted to better utilize the feature information for upsampling, which enhances the feature fusion capability of Insu-YOLO. Additionally, Insu-YOLO enhances the fusion between shallow and deep feature maps by adding an extra object detection layer, thereby increasing the accuracy for detecting small targets. The experimental results indicate that the mean average precision of Insu-YOLO reaches 95.9%, which is 3.95% higher than the YOLOv8n baseline model, with a memory usage of 9.2 MB. Moreover, the detection speed of Insu-YOLO is 87 frames/s which achieves the purpose of real-time identification of insulator defects.
Random-Access Neural Compression of Material Textures

The continuous advancement of photorealism in rendering is accompanied by a growth in texture data and, consequently, increasing storage and memory demands. To address this issue, we propose a novel neural compression technique specifically designed for material textures. We unlock two more levels of detail, i.e., 16x more texels, using low bitrate compression, with image quality that is better than advanced image compression techniques, such as AVIF and JPEG XL. At the same time, our method allows on-demand, real-time decompression with random access similar to block texture compression on GPUs, enabling compression on disk and memory. The key idea behind our approach is compressing multiple material textures and their mipmap chains together, and using a small neural network, that is optimized for each material, to decompress them. Finally, we use a custom training implementation to achieve practical compression speeds, whose performance surpasses that of general frameworks, like PyTorch, by an order of magnitude.
An overview on density peaks clustering

Density peaks clustering (DPC) algorithm is a new algorithm based on density clustering analysis, which can quickly obtain the cluster centers by drawing the decision diagram by using the calculation of local density and relative distance. Without prior knowledge and iteration, the parameters and structure are simple and easy to implement. Since it was proposed in 2014, it has attracted a large number of researchers to explore experiments and improve applications in recent years. In this paper, we first analyze the theory of DPC and its performance advantages and disadvantages. Secondly, it summarizes the improvement of DPC in recent years, analyzes the improvement effect, and shows it with experimental data. The related application research of DPC in different fields is introduced. Finally, the clustering results of DPC, LCDPC, DCHDPC and NADPC algorithms in different data sets are analyzed. Experiments show that the improved algorithm can divide the clustering more accurately, which provides new ideas for improving DPC algorithm in the future. At the same time, this paper summarizes and prospects the improvement and development of DPC.
Nonadiabatic holonomic quantum computation and its optimal control

Geometric phase has the intrinsic property of being resistant to some types of local noises as it only depends on global properties of the evolution path. Meanwhile, the non-Abelian geometric phase is in the matrix form, and thus can naturally be used to implement high performance quantum gates, i.e., the so-called holonomic quantum computation. This article reviews recent advances in nonadiabatic holonomic quantum computation, and focuses on various optimal control approaches that can improve the gate performance, in terms of the gate fidelity and robustness. Besides, we also pay special attention to its possible physical realizations and some concrete examples of experimental realizations. Finally, with all these efforts, within state-of-the-art technology, the performance of the implemented holonomic quantum gates can outperform the conventional dynamical ones, under certain conditions.
Explaining machine learning models with interactive natural language conversations using TalkToModel

Abstract Practitioners increasingly use machine learning (ML) models, yet models have become more complex and harder to understand. To understand complex models, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use explainability methods because they do not know which explanation to choose and how to interpret the explanation. Here we address the challenge of using explainability methods by proposing TalkToModel: an interactive dialogue system that explains ML models through natural language conversations. TalkToModel consists of three components: an adaptive dialogue engine that interprets natural language and generates meaningful responses; an execution component that constructs the explanations used in the conversation; and a conversational interface. In real-world evaluations, 73% of healthcare workers agreed they would use TalkToModel over existing systems for understanding a disease prediction model, and 85% of ML professionals agreed TalkToModel was easier to use, demonstrating that TalkToModel is highly effective for model explainability.
The potential use of non-fungible tokens (NFTs) in healthcare and medical research

Non-fungible tokens (NFTs) are cryptographic assets recorded on the blockchain that can certify authenticity and ownership, and they can be used to monetize health data, optimize the process of receiving a hematopoietic stem cell transplant, and improve the distribution of solid organs for transplantation. Blockchain technology, including NFTs, provides equitable access to wealth, increases transparency, eliminates personal or institutional biases of intermediaries, reduces inefficiencies, and ensures accountability. Blockchain architecture is ideal for ensuring security and privacy while granting individuals jurisdiction over their own information, making it a unique solution to the current limitations of existing health information systems. NFTs can be used to give patients the option to monetize their health data and provide valuable data to researchers. Wearable technology companies can also give their customers the option to monetize their data while providing data necessary to improve their products. Additionally, the process of receiving a hematopoietic stem cell transplant and the distribution of solid organs for transplantation could benefit from the integration of NFTs into the allocation process. However, there are limitations to the technology, including high energy consumption and the need for regulatory guidance. Further research is necessary to fully understand the potential of NFTs in healthcare and how it can be integrated with existing health information technology. Overall, NFTs have the potential to revolutionize the healthcare sector, providing benefits such as improved access to health information and increased efficiency in the distribution of organs for transplantation.
Geometric SMOTE for imbalanced datasets with nominal and continuous features

Imbalanced learning can be addressed in 3 different ways: Resampling, algorithmic modifications and cost-sensitive solutions. Resampling, and specifically oversampling, are more general approaches when opposed to algorithmic and cost-sensitive methods. Since the proposal of the Synthetic Minority Oversampling TEchnique (SMOTE), various SMOTE variants and neural network-based oversampling methods have been developed. However, the options to oversample datasets with nominal and continuous features are limited. We propose Geometric SMOTE for Nominal and Continuous features (G-SMOTENC), based on a combination of G-SMOTE and SMOTENC. Our method modifies SMOTENC's encoding and generation mechanism for nominal features while using G-SMOTE's data selection mechanism to determine the center observation and k-nearest neighbors and generation mechanism for continuous features. G-SMOTENC's performance is compared against SMOTENC's along with two other baseline methods, a State-of-the-art oversampling method and no oversampling. The experiment was performed over 20 datasets with varying imbalance ratios, number of metric and non-metric features and target classes. We found a significant improvement in classification performance when using G-SMOTENC as the oversampling method. An open-source implementation of G-SMOTENC is made available in the Python programming language.
An investigation of complex fuzzy sets for large-scale learning

Complex fuzzy sets are an extension of type-1 fuzzy sets with complex-valued membership functions. Over the last 20 years, time-series forecasting has emerged as the most important application of complex fuzzy sets, with neuro-fuzzy systems employing them shown to be accurate and compact forecasting models. In the complex fuzzy sets literature, two dominant approaches to designing forecasters can be observed: sinusoidal membership functions versus complex-valued Gaussian membership functions. To date, however, there has never been a systematic investigation that compares the performance of these two membership types (or their combination) within a common architecture. We propose a new neuro-fuzzy architecture using complex fuzzy sets that has been designed for large-scale learning problems. This architecture employs randomized learning to speed up network training. In designing this architecture, we empirically compared sinusoidal complex fuzzy sets and complex Gaussian fuzzy sets. Across multiple variations of the architecture, we find that the complex Gaussian fuzzy sets lead to significantly more accurate forecasts on moderate-to-large time series datasets, while still keeping the overall size of the network compact.
Refined Fredkin gate assisted by cross-Kerr nonlinearity

Resource-efficient quantum information processing is equal to the minimal cost of the quantum resources required by quantum logic gates to a certain extent. In this paper, we propose a refined scheme for setting up a polarized Fredkin gate assisted by weak cross-Kerr nonlinearity, X-homodyne detectors, and some linear optical elements. The realization of the Fredkin gate is definite and has a highly successful probability of being close to the unit, which is feasible with the current technology. Moreover, in contrast to the Fredkin gates that have been proposed, the scheme has the advantages of requiring less quantum resource without auxiliary photons, comparatively simplified circuits, lower requirements on the interaction strength, and robustness against photon loss, thus it can be used for scalable quantum computation.
SoK: Explainable Machine Learning for Computer Security Applications

Explainable Artificial Intelligence (XAI) aims to improve the transparency of machine learning (ML) pipelines.We systematize the increasingly growing (but fragmented) microcosm of studies that develop and utilize XAI methods for defensive and offensive cybersecurity tasks.We identify 3 cybersecurity stakeholders, i.e., model users, designers, and adversaries, who utilize XAI for 4 distinct objectives within an ML pipeline, namely 1) XAI-enabled user assistance, 2) XAI-enabled model verification, 3) explanation verification & robustness, and 4) offensive use of explanations.Our analysis of the literature indicates that many of the XAI applications are designed with little understanding of how they might be integrated into analyst workflowsuser studies for explanation evaluation are conducted in only 14% of the cases.The security literature sometimes also fails to disentangle the role of the various stakeholders, e.g., by providing explanations to model users and designers while also exposing them to adversaries.Additionally, the role of model designers is particularly minimized in the security literature.To this end, we present an illustrative tutorial for model designers, demonstrating how XAI can help with model verification.We also discuss scenarios where interpretability by design may be a better alternative.The systematization and the tutorial enable us to challenge several assumptions, and present open problems that can help shape the future of XAI research within cybersecurity.
Research Progress of Nature-Inspired Metaheuristic Algorithms in Mobile Robot Path Planning

The research of mobile robot path planning has shifted from the static environment to the dynamic environment, from the two-dimensional environment to the high-dimensional environment, and from the single-robot system to the multi-robot system. As the core technology for mobile robots to realize autonomous positioning and navigation, path-planning technology should plan collision-free and smooth paths for mobile robots in obstructed environments, which requires path-planning algorithms with a certain degree of intelligence. Metaheuristic algorithms are widely used in various optimization problems due to their algorithmic intelligence, and they have become the most effective algorithm to solve complex optimization problems in the field of mobile robot path planning. Based on a comprehensive analysis of existing path-planning algorithms, this paper proposes a new algorithm classification. Based on this classification, we focus on the firefly algorithm (FA) and the cuckoo search algorithm (CS), complemented by the dragonfly algorithm (DA), the whale optimization algorithm (WOA), and the sparrow search algorithm (SSA). During the analysis of the above algorithms, this paper summarizes the current research results of mobile robot path planning and proposes the future development trend of mobile robot path planning.
Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble Learning

In recent years, both machine learning and computer vision have seen growth in the use of multi-label categorization. SMOTE is now being utilized in existing research for data balance, and SMOTE does not consider that nearby examples may be from different classes when producing synthetic samples. As a result, there can be more class overlap and more noise. To avoid this problem, this work presented an innovative technique called Adaptive Synthetic Data-Based Multi-label Classification (ASDMLC). Adaptive Synthetic (ADASYN) sampling is a sampling strategy for learning from unbalanced data sets. ADASYN weights minority class instances by learning difficulty. For hard-to-learn minority class cases, synthetic data are created. Their numerical variables are normalized with the help of the Min-Max technique to standardize the magnitude of each variable's impact on the outcomes. The values of the attribute in this work are changed to a new range, from 0 to 1, using the normalization approach. To raise the accuracy of multi-label classification, Velocity-Equalized Particle Swarm Optimization (VPSO) is utilized for feature selection. In the proposed approach, to overcome the premature convergence problem, standard PSO has been improved by equalizing the velocity with each dimension of the problem. To expose the inherent label dependencies, the multi-label classification ensemble of Adaptive Neuro-Fuzzy Inference System (ANFIS), Probabilistic Neural Network (PNN), and Clustering-Based Decision tree methods will be processed based on an averaging method. The following criteria, including precision, recall, accuracy, and error rate, are used to assess performance. The suggested model's multi-label classification accuracy is 90.88%, better than previous techniques, which is PCT, HOMER, and ML-Forest is 65.57%, 70.66%, and 82.29%, respectively.
Enhancing the Sustainability of Smart Healthcare Applications with XAI

This chapter first mentions three ways to enhance the sustainability of smart healthcare applications. Then, our experience from the COVID-19 pandemic tells us that the three goals can actually be achieved by applying explainable artificial intelligence (XAI). After introducing some basics of XAI, some representative cases of XAI applications in medicine and healthcare in the literature are discussed. Subsequently, two well-known applications of XAI in healthcare are detailed: an XAI application for enhancing the sustainability of a ubiquitous clinic recommendation system and another XAI application for enhancing the sustainability of an ANN-based diabetes diagnosis system. The two XAI applications enhanced both the understanding and trust of users, making them willing to use the system again and contributing to the sustainability of the smart healthcare applications.